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DATE MATCH TIP ODDS RESULT 30.01.2021 Oldham – Salford City 2/1 31.00 0:1/2:1 WON 30.01.2021 Kilmarnock – St. Johnstone 1/2 26.00 2:0/2:3 WON Total odds: 806.00 WON 16.01.2021 Wolves – West Bromwich 1/2 51.00 2:1/2:3 WON 16.01.2021 Chorley – Farsley 2/1 21.00 1:2/3:2 WON Total odds: 1,071.00 WON 09.01.2021 Alloa – Cove Rangers 1/2 24.00 2:1/2:3 WON 09.01.2021 Gloucester City – Kidderminster 2/1 26.00 1:2/3:2 WON Total odds: 624.00 WON 02.01.2021 Huddersfield – Reading 1/2 34.00 1:0/1:2 WON 02.01.2021 Glenavon FC – Linfield 1/2 21.00 1:0/1:2 WON Total odds: 714.00 WON 19.12.2020 Cove Rangers – Clyde 1/2 39.00 2:1/2:3 WON 19.12.2020 Walsall – Port Vale 2/1 24.00 0:2/4:3 WON Total odds: 936.00 WON 12.12.2020 Leyton Orient – Newport County 2/1 29.00 0:1/2:1 WON 12.12.2020 Crewe – Northampton 2/1 21.00 0:1/2:1 WON Total odds: 609.00 WON 28.11.2020 Jonkopings Sodra – Trelleborgs 2/1 22.00 0:1/2:1 WON 28.11.2020 CSA – Ponte Preta 2/1 31.00 0:1/2:1 WON Total odds: 682.00 WON 21.11.2020 Ipswich – Shrewsbury 2/1 29.00 0:1/2:1 WON 21.11.2020 Chateauroux – Sochaux 2/1 31.00 0:1/2:1 WON Total odds: 899.00 WON 07.11.2020 Atletico PR – Fortaleza EC 2/1 29.00 0:1/2:1 WON 07.11.2020 VVV-Venlo – Heracles Almelo 2/1 34.00 0:1/3:2 WON Total odds: 1,326.00 WON 17.10.2020 Dalum IF – Middelfart 2/1 39.00 2:3/4:3 WON 17.10.2020 Volos NFC – Giannina PAS 2/1 29.00 0:1/2:1 WON Total odds: 1,131.00 WON 10.10.2020 Jerv – Raufoss 2/1 31.00 1:2/4:2 WON 10.10.2020 Kalisz – Bytovia Bytow 2/1 28.00 0:1/2:1 WON Total odds: 868.00 WON 03.10.2020 Peterborough – Swindon 2/1 26.00 0:1/3:1 WON 03.10.2020 Burton – Portsmouth 1/2 28.00 2:1/2:4 WON Total odds: 728.00 WON 26.09.2020 Bradford – Stevenage 2/1 27.00 0:1/2:1 WON 26.09.2020 Southend – Morecambe 1/2 29.00 1:0/1:2 WON Total odds: 783.00 WON 19.09.2020 Aarau – Wil 1/2 33.00 1:0/1:3 WON 19.09.2020 Rayo Vallecano – Sabadell 2/1 26.00 0:1/2:1 WON Total odds: 858.00 WON 12.09.2020 Cheltenham Town – Morecambe 1/2 36.00 1:0/1:2 WON 12.09.2020 Livingston – Hamilton 1/2 40.00 1:0/1:2 WON Total odds: 1,440.00 WON 05.09.2020 Municipal Grecia – Alajuelense 1/2 28.00 1:0/2:3 WON 05.09.2020 Municipal – Guastatoya 2/1 30.00 0:1/2:1 WON Total odds: 840.00 WON 29.08.2020 Arda – Botev Plovdiv 2/1 28.00 1:2/3:2 WON 29.08.2020 Aswan FC – Pyramids FC 1/2 24.00 1:0/1:2 WON Total odds: 672.00 WON 22.08.2020 Enskede – Hudiksvalls 1/2 28.00 1:0/1:4 WON 22.08.2020 Valerenga – Sandefjord 2/1 22.00 0:1/2:1 WON Total odds: 616.00 WON 15.08.2020 IFK Hassleholm – Asarums 1/2 25.00 2:1/3:4 WON 15.08.2020 Kanazawa – Ehime 2/1 26.00 0:1/3:2 WON Total odds: 650.00 WON 08.08.2020 Zweigen Kanazawa – Ehime 2/1 27.00 0:2/4:3 WON 08.08.2020 Daejeon Citizen – Gyeongnam 1/2 26.00 2:0/2:3 WON Total odds: 560.00 WON 01.08.2020 Napredak – Partizan 1/2 20.00 1:0/1:3 WON 01.08.2020 Incheon United – Gwangju 1/2 28.00 1:0/1:3 WON Total odds: 560.00 WON 18.07.2020 Moss – Eidsvold 1/2 39.00 1:0/1:2 WON 18.07.2020 Bryne – Flekkeroy 2/1 27.00 0:1/3:1 WON Total odds: 1.053.00 WON 11.07.2020 Gornik Leczna – Pogon Siedlce 1/2 32.00 1:0/2:3 WON 11.07.2020 Stomil Olsztyn – Miedz Legnica 1/2 23.00 1:0/1:2 WON Total odds: 736.00 WON 04.07.2020 Setubal – P. Ferreira 1/2 24.00 2:0/2:3 WON 04.07.2020 Vysocina Jihlava – Prostějov 2/1 22.00 0:1/3:2 WON Total odds: 528.00 WON 27.06.2020 Zwickau – Braunschweig 2/1 26.00 0:1/3:2 WON 27.06.2020 Bohemians 1905 – FC Slovacko 2/1 34.00 0:1/2:1 WON Total odds: 884.00 WON 07.03.2020 Shrewsbury – Oxford Utd 1/2 24.00 2:1/2:3 WON 07.03.2020 Crusaders – Institute 2/1 27.00 0:1/2:1 WON Total odds: 696.00 WON 22.02.2020 Municipal Vinto – Real Santa Cruz 2/1 22.00 0:1/2:1 WON 22.02.2020 Albacete – Numancia 2/1 24.00 0:1/2:1 WON Total odds: 696.00 WON 01.02.2020 Brechin – Elgin 1/2 25.00 1:0/1:2 WON 01.02.2020 Walsall – Crewe 1/2 24.00 1:0/1:2 WON Total odds: 696.00 WON 25.01.2020 Macclesfield – Forest Green 1/2 29.00 0:1/2:1 WON 25.01.2020 Crawley Town – Grimsby 2/1 24.00 1:2/3:2 WON Total odds: 696.00 WON 18.01.2020 Tranmere – Ipswich 1/2 23.00 1:0/1:2 WON 18.01.2020 Walsall – Crawley Town 2/1 25.00 0:1/2:1 WON Total odds: 754.00 WON 11.01.2020 Mansfield – Forest Green 1/2 31.00 2:1/3:4 WON 11.01.2020 Scunthorpe – Newport County 1/2 34.00 1:0/1:2 WON Total odds: 754.00 WON 28.12.2019 AFC Fylde – Stockport 1/2 29.00 1:0/1:2 WON 28.12.2019 Forfar Athletic – Montrose 1/2 26.00 1:0/2:3 WON Total odds: 754.00 WON 14.12.2019 Shrewsbury – Coventry 2/1 30.00 0:1/2:1 WON 14.12.2019 AFC Wimbledon – Doncaster 2/1 25.00 0:1/2:1 WON Total odds: 750.00 WON 23.11.2019 Milton Keynes – Rotherham 1/2 26.00 2:0/2:3 WON 23.11.2019 Karagumruk – Umraniyespor 1/2 29.00 1:0/2:3 WON Total odds: 754.00 WON 16.11.2019 Port Vale – Carlisle 2/1 27.00 0:1/2:1 WON 16.11.2019 Edinburgh City – Albion Rovers 2/1 23.00 0:1/3:2 WON Total odds: 621.00 WON 26.10.2019 Forfar Athletic – Airdrieonians 1/2 31.00 1:0/1:4 WON 26.10.2019 Preston – Blackburn 2/1 26.00 0:2/3:2 WON Total odds: 806.00 WON 05.10.2019 Oostende – Eupen 1/2 30.00 2:1/2:3 WON 05.10.2019 Tonbridge A. – Chelmsford City 1/2 24.00 1:0/1:2 WON Total odds: 720.00 WON.
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Date Match Pick Odds FT W/L 01.01.2021 Sheffield Wed – Derby 1 2.80 1:0 WIN 01.01.2021 Everton – West Ham 2 3.20 0:1 WIN 02.01.2021 Hull – Charlton 1 2.20 2:0 WIN 02.01.2021 Plymouth – Gillingham 1 2.30 1:0 WIN 03.01.2021 Eibar – Granada 1 2.60 2:0 WIN 03.01.2021 Aue – Braunschweig 1 2.00 3:1 WIN 04.01.2021 Cosenza – Empoli 2 2.20 0:2 WIN 04.01.2021 Ascoli – Reggina 1 2.90 2:1 WIN 05.01.2021 Mansfield – Salford 1 2.50 2:1 WIN 05.01.2021 Sochaux – Dunkerque 1 2.00 1:0 WIN 06.01.2021 Metz – Bordeaux X 3.00 0:0 WIN 06.01.2021 Marseille – Montpellier 1 2.10 3:1 WIN 07.01.2021 Fenerbahce – Alanyaspor 1 2.00 2:1 WIN 07.01.2021 Ankaraspor – Adanaspor AS 2 2.60 0:2 WIN 08.01.2021 Charlton – Accrington 2 3.00 0:2 WIN 08.01.2021 Sochaux – Clermont X 2.80 0:0 WIN 09.01.2021 Bordeaux – Lorient 1 2.10 2:1 WIN 09.01.2021 Metz – Nice X 3.20 1:1 WIN 10.01.2021 Levante – Eibar 1 2.30 2:1 WIN 10.01.2021 Cadiz CF – Alaves X 2.90 3:1 Lose 11.01.2021 Elche – Getafe 2 2.20 1:3 WIN 11.01.2021 Huesca – Betis 2 2.50 0:2 WIN 12.01.2021 Atl. Madrid – Sevilla 1 2.00 2:0 WIN 12.01.2021 Granada – Osasuna 1 2.30 2:0 WIN 13.01.2021 Den Haag – Venlo 2 2.50 1:4 WIN 13.01.2021 Den Haag – Venlo 2 2.50 1:4 WIN 14.01.2021 Atromitos – Lamia 1 2.00 2:1 WIN 14.01.2021 Aris – AEK Athens 2 2.40 0:1 WIN 15.01.2021 Red Star – Boulogne X 2.90 0:0 WIN 15.01.2021 Vicenza – Frosinone X 2.90 0:0 WIN 16.01.2021 Burton – Ipswich X 3.40 0:1 Lose 16.01.2021 Fleetwood – Portsmouth X 3.30 0:1 Lose 17.01.2021 Cercle Brugge – St. Liege 2 2.00 0:1 WIN 17.01.2021 Gent – Antwerp 2 3.50 0:1 WIN 18.01.2021 Munich 1860 – Ingolstadt 1 2.50 1:0 WIN 18.01.2021 Portimonense – Belenenses 1 2.40 1:0 WIN 19.01.2021 Peterborough – Charlton 1 2.20 2:1 WIN 19.01.2021 Reading – Coventry 1 2.20 3:0 WIN 20.01.2021 Betis – Celta Vigo 1 2.20 2:1 WIN 20.01.2021 Rizespor – Gaziantep 1 3.00 3:0 WIN 21.01.2021 Ankaragucu – Kasimpasa 1 2.40 1:0 WIN 21.01.2021 Valencia – Osasuna X 3.10 1:1 WIN 22.01.2021 Stoke – Watford 2 2.40 1:2 WIN 22.01.2021 Entella – Pisa 1 2.90 2:1 WIN 23.01.2021 Freiburg – Stuttgart 1 2.70 2:1 WIN 24.01.2021 Reims – Brest 1 2.50 1:0 WIN 24.01.2021 Metz – Nantes 1 2.60 2:0 WIN 25.01.2021 Le Mans – Red Star 1 3.00 2:1 WIN 25.01.2021 Belenenses – Tondela 1 2.20 2:0 WIN 26.01.2021 Gillingham – Crewe 1 2.90 4:1 WIN 26.01.2021 Scunthorpe – Port Vale 1 2.70 2:0 WIN 27.01.2021 Coventry – Sheffield Wed 1 2.20 2:0 WIN 27.01.2021 Barnsley – Cardiff 1 2.50 2:2 Lose 28.01.2021 St. Pauli – Bochum 2 2.20 2:3 WIN 28.01.2021 Sion – Basel 2 2.00 2:3 WIN 29.01.2021 Reading – Bournemouth 1 3.00 3:1 WIN 29.01.2021 Colchester – Scunthorpe 2 2.90 0:1 WIN 30.01.2021 Sittard – Venlo 1 2.10 3:2 WIN 30.01.2021 Eupen – Mouscron X 3.30 1:1 WIN 31.01.2021.
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DOUBLE FIXED MATCH HALFTIME/FULLTIME Date of HALFTIME/FULLTIME: 27.05.2018 Total odd for both fixed games : 335.00 STATUS: WON DENMARK: 3F Ligaen Women – Championship Fortuna Hjorring W vs VSK Aarhus W Pick: HT/FT 1-2 ; Result 1:0 / 1:2 ; Odd: 67.00.
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If you want to buy fixed matches 100% sure click here . Do you want just free daily predictions ? We have! click here .
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Our online website’s free daily tips use up our minimum resources and are just guesses or predictions made by our team of experienced experts with no backing behind them, so if you lose an investment using our website’s free services, we can not be held responsible because those tips or predictions or guesses have no guarantee backing them up.
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So before you decide to invest ANY money at all, you should very thoroughly go through every possible outcome of your investment so that you know that the investment (YOUR INVESTMENT) is worth the risk and that you’re willing to take that risk, because once you’ve invested your money there’s no going back, so be careful of WHAT, WHERE and WHEN to invest your money and HOW MUCH money you need to invest to get a worthy, positive return which would satisfy you, much like how much research you would do before proceeding to buy stocks of a company.
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Correct Score is a bet type that highlights the exact score of the outcome of a given match in soccer. Most bookmakers present it in many forms with the most being fixed to first half or full time. It usually one of he most crucial bet to predict but our Experts got you covered.
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Table below shows top most correct score tips and predictions for today.
Time League Match Pick 02:05 Colombia 02:05 Colombia - Primera A, Apertura, Round 4 Alianza Petrolera vs Rionegro Aguilas Doradas Correct Score HT(1-0) 04:00 Costa Rica 04:00 Costa Rica - Primera Division, Clausura, Round 6 San Carlos vs CS Cartagines Correct Score HT(1-0) 13:00 Turkey 13:00 Turkey - TFF 3. Lig, Grup 1, Round 17 Bucaspor 1928 vs Diyarbekirspor Correct Score FT(2-1) 14:30 Iran 14:30 Iran - Persian Gulf Pro League, Round 14 Tractor vs Persepolis Correct Score FT(0-1) 16:00 Uruguay 16:00 Uruguay - Primera DivisiГіn, Clausura, Round 4 Club Atletico River Plate vs Danubio Correct Score HT(1-0) 18:00 Ghana 18:00 Ghana - Premier League, Round 12 Medeama vs Accra Hearts of Oak Correct Score HT(0-0) 19:15 United Arab Emirates 19:15 United Arab Emirates - UAE Pro League, Round 15 Al-Wahda FC vs Al-Sharjah Correct Score FT(1-2) 19:30 Italy 19:30 Italy - Serie C, Girone B, Round 22 Mantova vs Padova Correct Score HT(0-1) 20:00 Guatemala 20:00 Guatemala - Liga Nacional, Apertura Playoffs, Final Deportivo Guastatoya vs CSD Municipal Correct Score FT(1-1) 20:30 World 20:30 World - Club World Cup, Round 2 Al Duhail vs Al Ahly Correct Score HT(0-0)
Its is usually abbreviated as "CS" and its annotation is as follows; CS HT(0-0), CS FT(2-1), CS HT(1-0), CS FT(1-1) etc. Most bookmakers have a huge number of odds placed on this bet type, hence our free tips helps your to increase your chances of winning on this bet.
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All correct score prediction of the matches scheduled to play tomorrow.
The correct score matches will be updated soon. Otherwise subscribe to our Expert tips or Premium tips.
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J League is highest football league in Japan and is founded in 1992 year, and also in one of the toughest football leagues in Asia. Contains 18 clubs which play 2 matches against each other - 1 as host and 1 as guest for a total of 34 matches per season. Clubs are ranked in table according to points they won. At the end of the season top 3 clubs receive the right to participate in AFC Champions League. The last two clubs are relegated to lower football league named J2 League, and their spots are taken by first two clubs from J2 League. The clubs finished on 16th place will play playoff against winner of J2 League play-off winner, and the winner from this match will take last free spot in J League for next season.




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DATE MATCH PICK ODD HT FT W/L 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // .
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DATE MATCH PICK ODD HT FT W/L 02.02.2021 Ballymena – Dungannon 2 7.00 // 0:1 WIN 02.02.2021 Palmeiras – Botafogo RJ X 4.50 // 1:1 WIN 02.02.2021 Ghazl El Mahallah – Zamalek 1 7.00 // 2:1 WIN 02.02.2021 Golden Arrows – Orlando Pirates 1/1 6.00 1:0 2:0 WIN.
VIP TICKET FOR 01.02.2021 BIG WIN 1.964.73 ODD.
DATE MATCH PICK ODD HT FT W/L 01.02.2021 CSM Slatina – U Craiova 1948 X/1 11.00 0:0 1:0 WIN 01.02.2021 Al Salmiya – Al Shabab 2/2 7.50 0:1 1:2 WIN 01.02.2021 Breda – Almere City 1/1 5.50 2:0 4:0 WIN 01.02.2021 Altay – Istanbulspor AS 2/2 4.33 0:2 1:3 WIN.
VIP TICKET FOR 30.01.2021 BIG WIN 2.437.50 ODD.
DATE MATCH PICK ODD HT FT W/L 30.01.2021 Western United – Melbourne Victory X/X 6.50 0:0 0:0 WIN 30.01.2021 Toulouse – Clermont X/1 5.00 1:1 3:2 WIN 30.01.2021 Regensburg – Darmstadt 2/X 15.00 0:1 1:1 WIN 30.01.2021 Verl – Meppen X/1 5.00 0:0 3:1 WIN.
VIP TICKET FOR 29.01.2021 BIG WIN 3.400.00 ODD.
DATE MATCH PICK ODD HT FT W/L 29.01.2021 Akhisarspor – Bandirmaspor 1 5.00 // 1:0 WIN 29.01.2021 Al-Ahli Manama – Al-Muharraq 1/1 8.50 1:0 1:0 WIN 29.01.2021 Omonia – Ol. Nicosia X/X 8.00 1:1 1:1 WIN 29.01.2021 Wurzburger Kickers – Dusseldorf X/1 10.00 1:1 2:1 WIN.
VIP TICKET FOR 28.01.2021 BIG WIN 2.805.00 ODD.
DATE MATCH PICK ODD HT FT W/L 28.01.2021 Chinandega U20 – Esteli U20 1 17.00 // 1:0 WIN 28.01.2021 St. Pauli – Bochum X/2 5.50 2:2 2:3 WIN 28.01.2021 CFR Cluj – Astra X 4.00 // 1:1 WIN 28.01.2021 Zrinjski (Bih) – Junak Sinj (Cro) 2 7.50 // 1:2 WIN.
VIP TICKET FOR 27.01.2021 BIG WIN 2.128.00 ODD.
DATE MATCH PICK ODD HT FT W/L 27.01.2021 Juve Stabia – Casertana 2/2 8.00 0:1 0:2 WIN 27.01.2021 Venlo – Vitesse 1 7.00 // 4:1 WIN 27.01.2021 AEK Athens FC – Giannina 2 8.00 // 0:2 WIN 27.01.2021 Kisvarda – MOL Fehervar 1 4.75 // 2:1 WIN.
VIP TICKET FOR 26.01.2021 BIG WIN 1.064.00 ODD.
DATE MATCH PICK ODD HT FT W/L 26.01.2021 Mouscron – Anderlecht X 4.00 // 1:1 WIN 26.01.2021 Tlemcen – MC Alger X/1 8.00 0:0 1:0 WIN 26.01.2021 Lok. Zagreb – Sibenik 2/2 4.75 0:2 0:4 WIN 26.01.2021 Aldershot – Weymouth 2 7.00 // 0:2 WIN.
VIP TICKET FOR 25.01.2021 BIG WIN 1.524.60 ODD.
DATE MATCH PICK ODD HT FT W/L 25.01.2021 Karmiotissa – Paphos X 3.30 // 0:0 WIN 25.01.2021 Sabah Baku – Kapaz 2 12.00 // 1:2 WIN 25.01.2021 La Equidad – Atl. Nacional X/1 7.00 0:0 1:0 WIN 25.01.2021 Sochaux – Toulouse X/2 5.50 0:0 0:1 WIN.
VIP TICKET FOR 23.01.2021 BIG WIN 1.443.75 ODD.
DATE MATCH PICK ODD HT FT W/L 23.01.2021 Wolfsberger AC – Hartberg X/X 7.00 0:0 0:0 WIN 23.01.2021 Varazdin – Rijeka 1 5.50 // 2:1 WIN 23.01.2021 Sao Paulo – Coritiba X 5.00 // 1:1 WIN 23.01.2021 Dunkerque – AC Ajaccio 1/1 7.50 2:0 3:1 WIN.
VIP TICKET FOR 22.01.2021 BIG WIN 1.800.00 ODD.
DATE MATCH PICK ODD HT FT W/L 22.01.2021 Anderlecht – Waasland-Beveren X/X 8.00 0:0 0:0 WIN 22.01.2021 VfL Osnabruck – Aue 2/2 5.00 0:1 0:1 WIN 22.01.2021 Xamax – Kriens X/2 9.00 0:0 0:1 WIN 22.01.2021 Sydney FC – Central Coast Mariners 2 5.00 // 0:2 WIN.
VIP TICKET FOR 21.01.2021 BIG WIN 2.062.50 ODD.
DATE MATCH PICK ODD HT FT W/L 21.01.2021 Coquimbo – Curico Unido X/X 5.50 0:0 0:0 WIN 21.01.2021 Valencia – Osasuna 2/X 15.00 0:1 1:1 WIN 21.01.2021 Sivasspor – Fenerbahce X/X 5.00 1:1 1:1 WIN 21.01.2021 Wanderers – Danubio X/1 5.00 0:0 2:0 WIN.
VIP TICKET FOR 20.01.2021 BIG WIN 1.670.62 ODD.
DATE MATCH PICK ODD HT FT W/L 20.01.2021 Arminia Bielefeld – Stuttgart 1 4.50 // 3:0 WIN 20.01.2021 Ol. Nicosia – Paralimni 2 4.50 // 0:1 WIN 20.01.2021 Guingamp – Caen 2/2 5.50 0:1 1:3 WIN 20.01.2021 Gremio – Atletico-MG 2/X 15.00 0:1 1:1 WIN.
VIP TICKET FOR 19.01.2021 BIG WIN 2.359.50 ODD.
DATE MATCH PICK ODD HT FT W/L 19.01.2021 Harrogate – Exeter X/X 5.50 0:0 0:0 WIN 19.01.2021 Sunderland – Plymouth 2/2 11.00 0:1 1:2 WIN 19.01.2021 FC Halifax – Southport 2 6.50 // 1:2 WIN 19.01.2021 Maidstone – Braintree 2 6.00 // 0:2 WIN.
VIP TICKET FOR 18.01.2021 BIG WIN 884.21 ODD.
DATE MATCH PICK ODD HT FT W/L 18.01.2021 Deportes Tolima – Once Caldas X/X 4.50 0:0 1:1 WIN 18.01.2021 Kasimpasa – Erzurum BB 2/2 6.50 0:1 1:2 WIN 18.01.2021 Chennaiyin – East Bengal X/X 5.50 0:0 0:0 WIN 18.01.2021 Estoril – Covilha X 5.50 // 0:0 WIN.
VIP TICKET FOR 16.01.2021 BIG WIN 3.330.93 ODD.
DATE MATCH PICK ODD HT FT W/L 16.01.2021 Dartford – Hungerford 2/X 15.00 0:1 1:1 WIN 16.01.2021 Mouscron – Genk 1 4.75 // 2:0 WIN 16.01.2021 Zwolle – Sittard X/2 8.50 0:0 0:2 WIN 16.01.2021 Dusseldorf II – Straelen 2 5.50 // 0:2 WIN.
VIP TICKET FOR 15.01.2021 BIG WIN 1.173.25 ODD.
DATE MATCH PICK ODD HT FT W/L 15.01.2021 La Serena – Union La Calera 2/2 4.75 0:1 1:3 WIN 15.01.2021 Biskra – Bel Abbes 2 8.00 // 0:1 WIN 15.01.2021 Al-Ahli Manama – Busaiteen X/X 4.75 0:0 0:0 WIN 15.01.2021 Paralimni – AEL Limassol 1 6.50 // 1:0 WIN.
VIP TICKET FOR 14.01.2021 BIG WIN 891.00 ODD.
DATE MATCH PICK ODD HT FT W/L 14.01.2021 Cruzeiro – Oeste 2 6.00 // 0:1 WIN 14.01.2021 Lanus – Velez Sarsfield 1/1 5.50 1:0 3:0 WIN 14.01.2021 Montevideo City – Rentistas 1/1 4.50 1:0 2:1 WIN 14.01.2021 Sonderjyske – Odense X/X 6.00 0:0 1:1 WIN.
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NEXT HALFTIME / FULL TIME FIXED MATCH.
DATE: 06 / 02 / 2021.
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TIP: 2/1 – 1/2.
ODD RANGE: 30.00 – 50.00.
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DATE MATCH PICK O DD HT/FT 28.03.2020 Arameisk-Syrianska (Swe) – IFK Stocksund (Swe) 1/2 41.00 1:0/1:2 WIN 04.04.2020 Istiqlol Dushanbe – Khujand 2/1 28.00 0:1/2:1 WIN 11.04.2020 Slutsk – Vitebsk Zhodino – Energetik-BGU 2/X X/1 15.00 5.00 0:1/1:1 WIN 0:0/2:0 WIN 18.04.2020 Orsha – Lida 2/1 51.00 0:1/3:2 WIN 25.04.2020 Faizkand – Istaravshan 2/1 23.00 0:1/2:1 WIN 03.05.2020 Vitebsk – Slavia Mozyr Taiwan Steel – Taichung 2/2 X/2 5.50 7.00 0:1/2:2 WIN 0:1/0:1 WIN 09.05.2020 Slavia Mozyr – Zhodino AB Argir – Vikingur X/X X/X 5.00 9.50 0:0/0:0 WIN 0:0/0:0 WIN 23.05.2020 23.05.2020 Sandhausen – Regensburg Vitebsk – Din. Minsk X/X X/X 6.00 4.75 0:0/0:0 WIN 1:1/1:1 WIN 30.05.2020 Belshina – Slavia Mozyr 1/2 31.00 2:1/2:3 WIN 06.06.2020 Sp. Subotica – Partizan 2/1 51.00 1:2/3:2 WIN 20.06.2020 Botev Plovdiv – Botev Vratsa 2/1 30.00 0:1/3:2 WIN 27.06.2020 Racing Santander – Albacete 1/2 36.00 1:0/1:2 WIN 04.07.2020 Racing Santander – Elche 1/2 30.00 1:0/1:2 WIN 11.07.2020 Dnipro-1 – Lviv 2/1 31.00 1:2/3:2 WIN 18.07.2020 Moss – Eidsvold 1/2 34.00 1:0/1:2 WIN 25.07.2020 Havant & W v Dartford 1/2 30.00 1:0/1:2 WIN 01.08.2020 Napredak – Partizan 1/2 25.00 1:0/1:3 WIN 22.08.2020 Sirius – Hammarby 2/1 30.00 0:1/2:1 WIN 29.08.2020 Tirol – Weindorf St. Anna 2/1 33.00 0:1/3:1 WIN 05.09.2020 Fleetwood – Wigan 2/1 30.00 1:2/3:2 WIN 12.09.2020 Din. Minsk – Dynamo Brest 1/2 35.00 1:0/2:4 WIN 19.09.2020 Hansa Rostock – Duisburg 2/1 28.00 0:1/3:1 WIN 26.09.2020 Bradford City – Stevenage 2/1 30.00 0:1/2:1 WIN 03.10.2020 Burton – Portsmouth 1/2 29.00 2:1/2:4 WIN 10.10.2020 Bromley – Torquay 1/2 35.00 1:0/1:2 WIN 17.10.2020 Notts Co – Maidenhead 1/2 41.00 2:1/2:3 WIN 24.10.2020 Morecambe – Forest Green 1/2 30.00 1:0/1:2 WIN 31.10.2020 Stockport – Weymouthd 1/2 51.00 1:0/1:2 WIN 07.11.2020 Venlo – Heracles 2/1 31.00 0:1/3:2 WIN 14.11.2020 Port Vale – Tranmere 1/2 41.00 2:0/3:4 WIN 21.11.2020 Ipswich – Shrewsbury 2/1 26.00 0:1/2:1 WIN 28.11.2020 AFC Fylde – Boston UTD 2/1 23.00 0:1/2:1 WIN 05.12.2020 Jagiellonia – Warta Poznan 2/1 26.00 2:3/4:3 WIN 12.12.2020 Sanat Naft – Gol Gohar 2/1 34.00 0:1/2:1 WIN 19.12.2020 Newport – Oldham 1/2 34.00 2:1/2:4 WIN 26.12.2020 Cardiff – Brentford 1/2 29.00 1:0/2:3 WIN 02.01.2021 Huddersfield – Reading 1/2 34.00 1:0/1:2 WIN 09.01.2021 Cambridge Utd – Harrogate 2/1 29.00 0:1/2:1 WIN 16.01.2021 Blackburn – Stoke Burton – Ipswich 2/X X/2 15.00 5.00 0:1/1:1 WIN 0:0/0:1 WIN 23.01.2021 Novara – Pro Vercelli 1/2 30.00 1:0/1:2 WIN 30.01.2021 Boreham Wood – Eastleigh 1/2 34.00 1:0/1:2 WIN 03.02.2021 CONTACT US AND BUY. HURRY LIMITED. ? ? ?
WHATS APP PROOF OF FIXED MATCH HALF TIME FULL TIME:
NEW WHATSAPP PROOF from (30/01) 34.00 ODDS WON.
NEW WHATSAPP PROOF from (23/01) 30.00 ODDS WON.
WHATSAPP PROOF from (16/01) 75.00 ODDS WON.


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NEXT VIP TICKET FOR 03.02.2021.
DATE MATCH PICK ODD HT FT W/L 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // .
VIP TICKET FOR 02.02.2021 BIG WIN 1.323.00 ODD.
DATE MATCH PICK ODD HT FT W/L 02.02.2021 Ballymena – Dungannon 2 7.00 // 0:1 WIN 02.02.2021 Palmeiras – Botafogo RJ X 4.50 // 1:1 WIN 02.02.2021 Ghazl El Mahallah – Zamalek 1 7.00 // 2:1 WIN 02.02.2021 Golden Arrows – Orlando Pirates 1/1 6.00 1:0 2:0 WIN.
VIP TICKET FOR 01.02.2021 BIG WIN 1.964.73 ODD.
DATE MATCH PICK ODD HT FT W/L 01.02.2021 CSM Slatina – U Craiova 1948 X/1 11.00 0:0 1:0 WIN 01.02.2021 Al Salmiya – Al Shabab 2/2 7.50 0:1 1:2 WIN 01.02.2021 Breda – Almere City 1/1 5.50 2:0 4:0 WIN 01.02.2021 Altay – Istanbulspor AS 2/2 4.33 0:2 1:3 WIN.
VIP TICKET FOR 30.01.2021 BIG WIN 2.437.50 ODD.
DATE MATCH PICK ODD HT FT W/L 30.01.2021 Western United – Melbourne Victory X/X 6.50 0:0 0:0 WIN 30.01.2021 Toulouse – Clermont X/1 5.00 1:1 3:2 WIN 30.01.2021 Regensburg – Darmstadt 2/X 15.00 0:1 1:1 WIN 30.01.2021 Verl – Meppen X/1 5.00 0:0 3:1 WIN.
VIP TICKET FOR 29.01.2021 BIG WIN 3.400.00 ODD.
DATE MATCH PICK ODD HT FT W/L 29.01.2021 Akhisarspor – Bandirmaspor 1 5.00 // 1:0 WIN 29.01.2021 Al-Ahli Manama – Al-Muharraq 1/1 8.50 1:0 1:0 WIN 29.01.2021 Omonia – Ol. Nicosia X/X 8.00 1:1 1:1 WIN 29.01.2021 Wurzburger Kickers – Dusseldorf X/1 10.00 1:1 2:1 WIN.
VIP TICKET FOR 28.01.2021 BIG WIN 2.805.00 ODD.
DATE MATCH PICK ODD HT FT W/L 28.01.2021 Chinandega U20 – Esteli U20 1 17.00 // 1:0 WIN 28.01.2021 St. Pauli – Bochum X/2 5.50 2:2 2:3 WIN 28.01.2021 CFR Cluj – Astra X 4.00 // 1:1 WIN 28.01.2021 Zrinjski (Bih) – Junak Sinj (Cro) 2 7.50 // 1:2 WIN.
VIP TICKET FOR 27.01.2021 BIG WIN 2.128.00 ODD.
DATE MATCH PICK ODD HT FT W/L 27.01.2021 Juve Stabia – Casertana 2/2 8.00 0:1 0:2 WIN 27.01.2021 Venlo – Vitesse 1 7.00 // 4:1 WIN 27.01.2021 AEK Athens FC – Giannina 2 8.00 // 0:2 WIN 27.01.2021 Kisvarda – MOL Fehervar 1 4.75 // 2:1 WIN.
VIP TICKET FOR 26.01.2021 BIG WIN 1.064.00 ODD.
DATE MATCH PICK ODD HT FT W/L 26.01.2021 Mouscron – Anderlecht X 4.00 // 1:1 WIN 26.01.2021 Tlemcen – MC Alger X/1 8.00 0:0 1:0 WIN 26.01.2021 Lok. Zagreb – Sibenik 2/2 4.75 0:2 0:4 WIN 26.01.2021 Aldershot – Weymouth 2 7.00 // 0:2 WIN.
VIP TICKET FOR 25.01.2021 BIG WIN 1.524.60 ODD.
DATE MATCH PICK ODD HT FT W/L 25.01.2021 Karmiotissa – Paphos X 3.30 // 0:0 WIN 25.01.2021 Sabah Baku – Kapaz 2 12.00 // 1:2 WIN 25.01.2021 La Equidad – Atl. Nacional X/1 7.00 0:0 1:0 WIN 25.01.2021 Sochaux – Toulouse X/2 5.50 0:0 0:1 WIN.
VIP TICKET FOR 23.01.2021 BIG WIN 1.443.75 ODD.
DATE MATCH PICK ODD HT FT W/L 23.01.2021 Wolfsberger AC – Hartberg X/X 7.00 0:0 0:0 WIN 23.01.2021 Varazdin – Rijeka 1 5.50 // 2:1 WIN 23.01.2021 Sao Paulo – Coritiba X 5.00 // 1:1 WIN 23.01.2021 Dunkerque – AC Ajaccio 1/1 7.50 2:0 3:1 WIN.
VIP TICKET FOR 22.01.2021 BIG WIN 1.800.00 ODD.
DATE MATCH PICK ODD HT FT W/L 22.01.2021 Anderlecht – Waasland-Beveren X/X 8.00 0:0 0:0 WIN 22.01.2021 VfL Osnabruck – Aue 2/2 5.00 0:1 0:1 WIN 22.01.2021 Xamax – Kriens X/2 9.00 0:0 0:1 WIN 22.01.2021 Sydney FC – Central Coast Mariners 2 5.00 // 0:2 WIN.
VIP TICKET FOR 21.01.2021 BIG WIN 2.062.50 ODD.
DATE MATCH PICK ODD HT FT W/L 21.01.2021 Coquimbo – Curico Unido X/X 5.50 0:0 0:0 WIN 21.01.2021 Valencia – Osasuna 2/X 15.00 0:1 1:1 WIN 21.01.2021 Sivasspor – Fenerbahce X/X 5.00 1:1 1:1 WIN 21.01.2021 Wanderers – Danubio X/1 5.00 0:0 2:0 WIN.
VIP TICKET FOR 20.01.2021 BIG WIN 1.670.62 ODD.
DATE MATCH PICK ODD HT FT W/L 20.01.2021 Arminia Bielefeld – Stuttgart 1 4.50 // 3:0 WIN 20.01.2021 Ol. Nicosia – Paralimni 2 4.50 // 0:1 WIN 20.01.2021 Guingamp – Caen 2/2 5.50 0:1 1:3 WIN 20.01.2021 Gremio – Atletico-MG 2/X 15.00 0:1 1:1 WIN.
VIP TICKET FOR 19.01.2021 BIG WIN 2.359.50 ODD.
DATE MATCH PICK ODD HT FT W/L 19.01.2021 Harrogate – Exeter X/X 5.50 0:0 0:0 WIN 19.01.2021 Sunderland – Plymouth 2/2 11.00 0:1 1:2 WIN 19.01.2021 FC Halifax – Southport 2 6.50 // 1:2 WIN 19.01.2021 Maidstone – Braintree 2 6.00 // 0:2 WIN.
VIP TICKET FOR 18.01.2021 BIG WIN 884.21 ODD.
DATE MATCH PICK ODD HT FT W/L 18.01.2021 Deportes Tolima – Once Caldas X/X 4.50 0:0 1:1 WIN 18.01.2021 Kasimpasa – Erzurum BB 2/2 6.50 0:1 1:2 WIN 18.01.2021 Chennaiyin – East Bengal X/X 5.50 0:0 0:0 WIN 18.01.2021 Estoril – Covilha X 5.50 // 0:0 WIN.
VIP TICKET FOR 16.01.2021 BIG WIN 3.330.93 ODD.
DATE MATCH PICK ODD HT FT W/L 16.01.2021 Dartford – Hungerford 2/X 15.00 0:1 1:1 WIN 16.01.2021 Mouscron – Genk 1 4.75 // 2:0 WIN 16.01.2021 Zwolle – Sittard X/2 8.50 0:0 0:2 WIN 16.01.2021 Dusseldorf II – Straelen 2 5.50 // 0:2 WIN.
VIP TICKET FOR 15.01.2021 BIG WIN 1.173.25 ODD.
DATE MATCH PICK ODD HT FT W/L 15.01.2021 La Serena – Union La Calera 2/2 4.75 0:1 1:3 WIN 15.01.2021 Biskra – Bel Abbes 2 8.00 // 0:1 WIN 15.01.2021 Al-Ahli Manama – Busaiteen X/X 4.75 0:0 0:0 WIN 15.01.2021 Paralimni – AEL Limassol 1 6.50 // 1:0 WIN.
VIP TICKET FOR 14.01.2021 BIG WIN 891.00 ODD.
DATE MATCH PICK ODD HT FT W/L 14.01.2021 Cruzeiro – Oeste 2 6.00 // 0:1 WIN 14.01.2021 Lanus – Velez Sarsfield 1/1 5.50 1:0 3:0 WIN 14.01.2021 Montevideo City – Rentistas 1/1 4.50 1:0 2:1 WIN 14.01.2021 Sonderjyske – Odense X/X 6.00 0:0 1:1 WIN.
If you want see all archives from our sold Vip Tickets, please CLICK HERE.


Payment methods: Skrill, Neteller,PayPal, M-Pesa, PaySafe Card and BitCoin. – No additional payments requested. – Matches received from inside the clubs. – Matches have high stake of wining , no chance for lose. IMPORTANT: All informations for matchs is sure and safe 100%.
NEXT VIP TICKET FOR 03.02.2021.
DATE MATCH PICK ODD HT FT W/L 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // . 03.02.2021 . – . // // // // .
VIP TICKET FOR 02.02.2021 BIG WIN 1.323.00 ODD.
DATE MATCH PICK ODD HT FT W/L 02.02.2021 Ballymena – Dungannon 2 7.00 // 0:1 WIN 02.02.2021 Palmeiras – Botafogo RJ X 4.50 // 1:1 WIN 02.02.2021 Ghazl El Mahallah – Zamalek 1 7.00 // 2:1 WIN 02.02.2021 Golden Arrows – Orlando Pirates 1/1 6.00 1:0 2:0 WIN.
VIP TICKET FOR 01.02.2021 BIG WIN 1.964.73 ODD.
DATE MATCH PICK ODD HT FT W/L 01.02.2021 CSM Slatina – U Craiova 1948 X/1 11.00 0:0 1:0 WIN 01.02.2021 Al Salmiya – Al Shabab 2/2 7.50 0:1 1:2 WIN 01.02.2021 Breda – Almere City 1/1 5.50 2:0 4:0 WIN 01.02.2021 Altay – Istanbulspor AS 2/2 4.33 0:2 1:3 WIN.
VIP TICKET FOR 30.01.2021 BIG WIN 2.437.50 ODD.
DATE MATCH PICK ODD HT FT W/L 30.01.2021 Western United – Melbourne Victory X/X 6.50 0:0 0:0 WIN 30.01.2021 Toulouse – Clermont X/1 5.00 1:1 3:2 WIN 30.01.2021 Regensburg – Darmstadt 2/X 15.00 0:1 1:1 WIN 30.01.2021 Verl – Meppen X/1 5.00 0:0 3:1 WIN.
VIP TICKET FOR 29.01.2021 BIG WIN 3.400.00 ODD.
DATE MATCH PICK ODD HT FT W/L 29.01.2021 Akhisarspor – Bandirmaspor 1 5.00 // 1:0 WIN 29.01.2021 Al-Ahli Manama – Al-Muharraq 1/1 8.50 1:0 1:0 WIN 29.01.2021 Omonia – Ol. Nicosia X/X 8.00 1:1 1:1 WIN 29.01.2021 Wurzburger Kickers – Dusseldorf X/1 10.00 1:1 2:1 WIN.
VIP TICKET FOR 28.01.2021 BIG WIN 2.805.00 ODD.
DATE MATCH PICK ODD HT FT W/L 28.01.2021 Chinandega U20 – Esteli U20 1 17.00 // 1:0 WIN 28.01.2021 St. Pauli – Bochum X/2 5.50 2:2 2:3 WIN 28.01.2021 CFR Cluj – Astra X 4.00 // 1:1 WIN 28.01.2021 Zrinjski (Bih) – Junak Sinj (Cro) 2 7.50 // 1:2 WIN.
VIP TICKET FOR 27.01.2021 BIG WIN 2.128.00 ODD.
DATE MATCH PICK ODD HT FT W/L 27.01.2021 Juve Stabia – Casertana 2/2 8.00 0:1 0:2 WIN 27.01.2021 Venlo – Vitesse 1 7.00 // 4:1 WIN 27.01.2021 AEK Athens FC – Giannina 2 8.00 // 0:2 WIN 27.01.2021 Kisvarda – MOL Fehervar 1 4.75 // 2:1 WIN.
VIP TICKET FOR 26.01.2021 BIG WIN 1.064.00 ODD.
DATE MATCH PICK ODD HT FT W/L 26.01.2021 Mouscron – Anderlecht X 4.00 // 1:1 WIN 26.01.2021 Tlemcen – MC Alger X/1 8.00 0:0 1:0 WIN 26.01.2021 Lok. Zagreb – Sibenik 2/2 4.75 0:2 0:4 WIN 26.01.2021 Aldershot – Weymouth 2 7.00 // 0:2 WIN.
VIP TICKET FOR 25.01.2021 BIG WIN 1.524.60 ODD.
DATE MATCH PICK ODD HT FT W/L 25.01.2021 Karmiotissa – Paphos X 3.30 // 0:0 WIN 25.01.2021 Sabah Baku – Kapaz 2 12.00 // 1:2 WIN 25.01.2021 La Equidad – Atl. Nacional X/1 7.00 0:0 1:0 WIN 25.01.2021 Sochaux – Toulouse X/2 5.50 0:0 0:1 WIN.
VIP TICKET FOR 23.01.2021 BIG WIN 1.443.75 ODD.
DATE MATCH PICK ODD HT FT W/L 23.01.2021 Wolfsberger AC – Hartberg X/X 7.00 0:0 0:0 WIN 23.01.2021 Varazdin – Rijeka 1 5.50 // 2:1 WIN 23.01.2021 Sao Paulo – Coritiba X 5.00 // 1:1 WIN 23.01.2021 Dunkerque – AC Ajaccio 1/1 7.50 2:0 3:1 WIN.
VIP TICKET FOR 22.01.2021 BIG WIN 1.800.00 ODD.
DATE MATCH PICK ODD HT FT W/L 22.01.2021 Anderlecht – Waasland-Beveren X/X 8.00 0:0 0:0 WIN 22.01.2021 VfL Osnabruck – Aue 2/2 5.00 0:1 0:1 WIN 22.01.2021 Xamax – Kriens X/2 9.00 0:0 0:1 WIN 22.01.2021 Sydney FC – Central Coast Mariners 2 5.00 // 0:2 WIN.
VIP TICKET FOR 21.01.2021 BIG WIN 2.062.50 ODD.
DATE MATCH PICK ODD HT FT W/L 21.01.2021 Coquimbo – Curico Unido X/X 5.50 0:0 0:0 WIN 21.01.2021 Valencia – Osasuna 2/X 15.00 0:1 1:1 WIN 21.01.2021 Sivasspor – Fenerbahce X/X 5.00 1:1 1:1 WIN 21.01.2021 Wanderers – Danubio X/1 5.00 0:0 2:0 WIN.
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DATE MATCH PICK ODD HT FT W/L 20.01.2021 Arminia Bielefeld – Stuttgart 1 4.50 // 3:0 WIN 20.01.2021 Ol. Nicosia – Paralimni 2 4.50 // 0:1 WIN 20.01.2021 Guingamp – Caen 2/2 5.50 0:1 1:3 WIN 20.01.2021 Gremio – Atletico-MG 2/X 15.00 0:1 1:1 WIN.
VIP TICKET FOR 19.01.2021 BIG WIN 2.359.50 ODD.
DATE MATCH PICK ODD HT FT W/L 19.01.2021 Harrogate – Exeter X/X 5.50 0:0 0:0 WIN 19.01.2021 Sunderland – Plymouth 2/2 11.00 0:1 1:2 WIN 19.01.2021 FC Halifax – Southport 2 6.50 // 1:2 WIN 19.01.2021 Maidstone – Braintree 2 6.00 // 0:2 WIN.
VIP TICKET FOR 18.01.2021 BIG WIN 884.21 ODD.
DATE MATCH PICK ODD HT FT W/L 18.01.2021 Deportes Tolima – Once Caldas X/X 4.50 0:0 1:1 WIN 18.01.2021 Kasimpasa – Erzurum BB 2/2 6.50 0:1 1:2 WIN 18.01.2021 Chennaiyin – East Bengal X/X 5.50 0:0 0:0 WIN 18.01.2021 Estoril – Covilha X 5.50 // 0:0 WIN.
VIP TICKET FOR 16.01.2021 BIG WIN 3.330.93 ODD.
DATE MATCH PICK ODD HT FT W/L 16.01.2021 Dartford – Hungerford 2/X 15.00 0:1 1:1 WIN 16.01.2021 Mouscron – Genk 1 4.75 // 2:0 WIN 16.01.2021 Zwolle – Sittard X/2 8.50 0:0 0:2 WIN 16.01.2021 Dusseldorf II – Straelen 2 5.50 // 0:2 WIN.
VIP TICKET FOR 15.01.2021 BIG WIN 1.173.25 ODD.
DATE MATCH PICK ODD HT FT W/L 15.01.2021 La Serena – Union La Calera 2/2 4.75 0:1 1:3 WIN 15.01.2021 Biskra – Bel Abbes 2 8.00 // 0:1 WIN 15.01.2021 Al-Ahli Manama – Busaiteen X/X 4.75 0:0 0:0 WIN 15.01.2021 Paralimni – AEL Limassol 1 6.50 // 1:0 WIN.
VIP TICKET FOR 14.01.2021 BIG WIN 891.00 ODD.
DATE MATCH PICK ODD HT FT W/L 14.01.2021 Cruzeiro – Oeste 2 6.00 // 0:1 WIN 14.01.2021 Lanus – Velez Sarsfield 1/1 5.50 1:0 3:0 WIN 14.01.2021 Montevideo City – Rentistas 1/1 4.50 1:0 2:1 WIN 14.01.2021 Sonderjyske – Odense X/X 6.00 0:0 1:1 WIN.
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http://forum.shopheroesgame.com/viewtopi...4&t=164533
http://gornoprom.ru/marketplace/discussions/21206
https://adminclub.org/Thread-fixed-matches-256
http://institucional.us.es/delepsico/for...9#p1183459
https://bitexpert.club/viewtopic.php?f=9&t=145176
http://armor-team.com/members/unyemarry/
https://ratemycups.com/profile/Unygroot
https://forum.devnagri.com/posting.php?m...dea0c2a%20
https://worldsim.club/showthread.php?tid...1#pid14421
https://worldsim.club/showthread.php?tid...0#pid14560
https://share1s.net/posting.php?mode=rep...&t=1058748
http://forum.shopheroesgame.com/viewtopi...4&t=164627
https://www.mac-it.ch/forum/showthread.php?tid=273054
http://biosfix.net/forums/1/posts/create
http://hrpcs.org/showthread.php?tid=3381...#pid219125
http://www.sp3pje.ayz.pl/forum/showthrea...tid=272718
https://www.progettoarte.info/index.php?...&id=325663
https://forum.cctvtechforum.com/Thread-r...es--520743

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п»їPredicting Football Results With Statistical Modelling.
Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution.
David Sheehan.
Data scientist interested in sports, politics and Simpsons references.
Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house.
Poisson Distribution.
HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1.
We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches).
You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer:
represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions.
We can use this statistical model to estimate the probability of specfic events.
The probability of a draw is simply the sum of the events where the two teams score the same amount of goals.
Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution.
So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively).
Building A Model.
You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?).
Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421.
If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense.
Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.


Predicting Football Results With Statistical Modelling.
Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution.
David Sheehan.
Data scientist interested in sports, politics and Simpsons references.
Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house.
Poisson Distribution.
HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1.
We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches).
You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer:
represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions.
We can use this statistical model to estimate the probability of specfic events.
The probability of a draw is simply the sum of the events where the two teams score the same amount of goals.
Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution.
So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively).
Building A Model.
You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?).
Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421.
If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense.
Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.


Predicting Football Results With Statistical Modelling.
Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution.
David Sheehan.
Data scientist interested in sports, politics and Simpsons references.
Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house.
Poisson Distribution.
HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1.
We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches).
You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer:
represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions.
We can use this statistical model to estimate the probability of specfic events.
The probability of a draw is simply the sum of the events where the two teams score the same amount of goals.
Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution.
So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively).
Building A Model.
You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?).
Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421.
If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense.
Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.


Predicting Football Results With Statistical Modelling.
Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution.
David Sheehan.
Data scientist interested in sports, politics and Simpsons references.
Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house.
Poisson Distribution.
HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1.
We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches).
You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer:
represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions.
We can use this statistical model to estimate the probability of specfic events.
The probability of a draw is simply the sum of the events where the two teams score the same amount of goals.
Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution.
So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively).
Building A Model.
You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?).
Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421.
If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense.
Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.


Predicting Football Results With Statistical Modelling.
Combining the world’s most popular sport with everyone’s favourite discrete probability distribution, this post predicts football matches using the Poisson distribution.
David Sheehan.
Data scientist interested in sports, politics and Simpsons references.
Football (or soccer to my American readers) is full of clichés: “It’s a game of two halves”, “taking it one game at a time” and “Liverpool have failed to win the Premier League”. You’re less likely to hear “Treating the number of goals scored by each team as independent Poisson processes, statistical modelling suggests that the home team have a 60% chance of winning today”. But this is actually a bit of cliché too (it has been discussed here, here, here, here and particularly well here). As we’ll discover, a simple Poisson model is, well, overly simplistic. But it’s a good starting point and a nice intuitive way to learn about statistical modelling. So, if you came here looking to make money, I hear this guy makes £5000 per month without leaving the house.
Poisson Distribution.
HomeTeam AwayTeam HomeGoals AwayGoals 0 Burnley Swansea 0 1 1 Crystal Palace West Brom 0 1 2 Everton Tottenham 1 1 3 Hull Leicester 2 1 4 Man City Sunderland 2 1.
We imported a csv as a pandas dataframe, which contains various information for each of the 380 EPL games in the 2016-17 English Premier League season. We restricted the dataframe to the columns in which we’re interested (specifically, team names and numer of goals scored by each team). I’ll omit most of the code that produces the graphs in this post. But don’t worry, you can find that code on my github page. Our task is to model the final round of fixtures in the season, so we must remove the last 10 rows (each gameweek consists of 10 matches).
You’ll notice that, on average, the home team scores more goals than the away team. This is the so called ‘home (field) advantage’ (discussed here) and isn’t specific to soccer. This is a convenient time to introduce the Poisson distribution. It’s a discrete probability distribution that describes the probability of the number of events within a specific time period (e.g 90 mins) with a known average rate of occurrence. A key assumption is that the number of events is independent of time. In our context, this means that goals don’t become more/less likely by the number of goals already scored in the match. Instead, the number of goals is expressed purely as function an average rate of goals. If that was unclear, maybe this mathematical formulation will make clearer:
represents the average rate (e.g. average number of goals, average number of letters you receive, etc.). So, we can treat the number of goals scored by the home and away team as two independent Poisson distributions. The plot below shows the proportion of goals scored compared to the number of goals estimated by the corresponding Poisson distributions.
We can use this statistical model to estimate the probability of specfic events.
The probability of a draw is simply the sum of the events where the two teams score the same amount of goals.
Note that we consider the number of goals scored by each team to be independent events (i.e. P(A n B) = P(A) P(B)). The difference of two Poisson distribution is actually called a Skellam distribution. So we can calculate the probability of a draw by inputting the mean goal values into this distribution.
So, hopefully you can see how we can adapt this approach to model specific matches. We just need to know the average number of goals scored by each team and feed this data into a Poisson model. Let’s have a look at the distribution of goals scored by Chelsea and Sunderland (teams who finished 1st and last, respectively).
Building A Model.
You should now be convinced that the number of goals scored by each team can be approximated by a Poisson distribution. Due to a relatively sample size (each team plays at most 19 home/away games), the accuracy of this approximation can vary significantly (especially earlier in the season when teams have played fewer games). Similar to before, we could now calculate the probability of various events in this Chelsea Sunderland match. But rather than treat each match separately, we’ll build a more general Poisson regression model (what is that?).
Generalized Linear Model Regression Results Dep. Variable: goals No. Observations: 740 Model: GLM Df Residuals: 700 Model Family: Poisson Df Model: 39 Link Function: log Scale: 1.0 Method: IRLS Log-Likelihood: -1042.4 Date: Sat, 10 Jun 2017 Deviance: 776.11 Time: 11:17:38 Pearson chi2: 659. No. Iterations: 8 coef std err z P>|z| [95.0% Conf. Int.] Intercept 0.3725 0.198 1.880 0.060 -0.016 0.761 team[T.Bournemouth] -0.2891 0.179 -1.612 0.107 -0.641 0.062 team[T.Burnley] -0.6458 0.200 -3.230 0.001 -1.038 -0.254 team[T.Chelsea] 0.0789 0.162 0.488 0.626 -0.238 0.396 team[T.Crystal Palace] -0.3865 0.183 -2.107 0.035 -0.746 -0.027 team[T.Everton] -0.2008 0.173 -1.161 0.246 -0.540 0.138 team[T.Hull] -0.7006 0.204 -3.441 0.001 -1.100 -0.302 team[T.Leicester] -0.4204 0.187 -2.249 0.025 -0.787 -0.054 team[T.Liverpool] 0.0162 0.164 0.099 0.921 -0.306 0.338 team[T.Man City] 0.0117 0.164 0.072 0.943 -0.310 0.334 team[T.Man United] -0.3572 0.181 -1.971 0.049 -0.713 -0.002 team[T.Middlesbrough] -1.0087 0.225 -4.481 0.000 -1.450 -0.568 team[T.Southampton] -0.5804 0.195 -2.976 0.003 -0.963 -0.198 team[T.Stoke] -0.6082 0.197 -3.094 0.002 -0.994 -0.223 team[T.Sunderland] -0.9619 0.222 -4.329 0.000 -1.397 -0.526 team[T.Swansea] -0.5136 0.192 -2.673 0.008 -0.890 -0.137 team[T.Tottenham] 0.0532 0.162 0.328 0.743 -0.265 0.371 team[T.Watford] -0.5969 0.197 -3.035 0.002 -0.982 -0.211 team[T.West Brom] -0.5567 0.194 -2.876 0.004 -0.936 -0.177 team[T.West Ham] -0.4802 0.189 -2.535 0.011 -0.851 -0.109 opponent[T.Bournemouth] 0.4109 0.196 2.092 0.036 0.026 0.796 opponent[T.Burnley] 0.1657 0.206 0.806 0.420 -0.237 0.569 opponent[T.Chelsea] -0.3036 0.234 -1.298 0.194 -0.762 0.155 opponent[T.Crystal Palace] 0.3287 0.200 1.647 0.100 -0.062 0.720 opponent[T.Everton] -0.0442 0.218 -0.202 0.840 -0.472 0.384 opponent[T.Hull] 0.4979 0.193 2.585 0.010 0.120 0.875 opponent[T.Leicester] 0.3369 0.199 1.694 0.090 -0.053 0.727 opponent[T.Liverpool] -0.0374 0.217 -0.172 0.863 -0.463 0.389 opponent[T.Man City] -0.0993 0.222 -0.448 0.654 -0.534 0.335 opponent[T.Man United] -0.4220 0.241 -1.754 0.079 -0.894 0.050 opponent[T.Middlesbrough] 0.1196 0.208 0.574 0.566 -0.289 0.528 opponent[T.Southampton] 0.0458 0.211 0.217 0.828 -0.369 0.460 opponent[T.Stoke] 0.2266 0.203 1.115 0.265 -0.172 0.625 opponent[T.Sunderland] 0.3707 0.198 1.876 0.061 -0.017 0.758 opponent[T.Swansea] 0.4336 0.195 2.227 0.026 0.052 0.815 opponent[T.Tottenham] -0.5431 0.252 -2.156 0.031 -1.037 -0.049 opponent[T.Watford] 0.3533 0.198 1.782 0.075 -0.035 0.742 opponent[T.West Brom] 0.0970 0.209 0.463 0.643 -0.313 0.507 opponent[T.West Ham] 0.3485 0.198 1.758 0.079 -0.040 0.737 home 0.2969 0.063 4.702 0.000 0.173 0.421.
If you’re curious about the smf.glm(. ) part, you can find more information here (edit: earlier versions of this post had erroneously employed a Generalised Estimating Equation (GEE)- what’s the difference?). I’m more interested in the values presented in the coef column in the model summary table, which are analogous to the slopes in linear regression. Similar to logistic regression, we take the exponent of the parameter values. A positive value implies more goals (), while values closer to zero represent more neutral effects (). Towards the bottom of the table you might notice that home has a coef of 0.2969. This captures the fact that home teams generally score more goals than the away team (specifically, =1.35 times more likely). But not all teams are created equal. Chelsea has a coef of 0.0789, while the corresponding value for Sunderland is -0.9619 (sort of saying Chelsea (Sunderland) are better (much worse!) scorers than average). Finally, the opponent* values penalize/reward teams based on the quality of the opposition. This relfects the defensive strength of each team (Chelsea: -0.3036; Sunderland: 0.3707). In other words, you’re less likely to score against Chelsea. Hopefully, that all makes both statistical and intuitive sense.
Let’s start making some predictions for the upcoming matches. We simply pass our teams into poisson_model and it’ll return the expected average number of goals for that team (we need to run it twice- we calculate the expected average number of goals for each team separately). So let’s see how many goals we expect Chelsea and Sunderland to score.




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п»їCorrect Score Betting Tips.
Are you looking for winning Correct Score Betting Tips to help you make a chunky profit from your betting? We have had winners at odds ranging from 40/1 all the way up to 360/1. On an average day the odds of our correct score tips will usually be around 90/1 so a ВЈ10 bet would return over ВЈ900.
Want to See ALL the Correct Score Predictions?
Predictions price in other currencies: 520 NGN, 149 KES - We accept card payments and Paypal.
Want More Betting Tips?
Here are some of the most popular areas of the site that you may also want to check out whilst you are here picking your bets:
If you are serious about making a profit from your football betting, then you NEED to take a look at our PRO TIPS section where we give you the exact bets you need to place every day to make a consistent profit from your betting.
Are you ready to make a profit from your football betting?
What Is Correct Score Betting?
With Correct Score betting you are trying to pick the exact final score of a football match. Rather than simply picking a team to win you need to also correctly predict the exact number of goals each team is going to score in the game. This means that you will get much better odds due to the complexity of the bet. As an example, Man City to beat Watford might be available at odds of 1.50 (1/2) - however, Man City to win 3-0 may be at odds of 8.00 (7/1) which offers much better value.
How To Pick Correct Score Bets?
Correct score bets are very difficult to predict consistently however if you use a methodical approach you can narrow down the number of options. Firstly you need to narrow down whether or not you think both team will score in the match as if you think they will then this will remove all the win-to-nil options like 1-0, 2-0, etc. Once you have made a decision on BTTS it is then time to decide on the number of goals in the match, we tend to look at whether it will be over or under 2.5 goals. Finally we need to combine our goals and BTTS thoughts with who we think will win the game. As an example using the Man City vs Watford game mentioned above - Chance of BTTS = Yes, Chance of Over 2.5 Goals = Yes, Likely Winner = Man City, this leaves us with the follow best predictions 2-1, 3-1, 4-1. You can either use your own judgement at this point or potentially could look at any previous meetings or previous winning margins to narrow the selections further. As Man City normally win by more than one goal at home the 3-1 prediction would be most likely. As you can see it is a fairly long process - which is why having an algorithm do it for you is beneficial. Use our tips above to save time and win more correct score bets.
Best Way To Use Correct Score Tips.
Using the above correct score tips in singles is an obvious option given that the average odds will likely be around 7.00 (6/1), however, the most profitable way of doing may actually be to build out some multi bets and accumulators. Personally, we like to combine them into Correct Score Doubles, as these are lower in risk and can have odds around 49.00 (48/1) which wins ВЈ480 from a ВЈ10 bet. Another option we like to employ is to pick five correct score tips and combine them as doubles. This will create a ten line bet, so ВЈ1 a line will have a ВЈ10 total stake and you only need two out of the five to come in for a decent profit, any more than two winners will deliver a huge profit. If you are really confident you can go for trebles for average odds per line of around 343.00 (342/1)!
What is a Correct Score Double?
A Correct Score Double is when you select two correct score picks and back them together to create a higher odds bet that needs both correct scores to be correct in order to win. These can be very attractive for punters as the returns can be very high however, the risk is also very high as well so don't invest heavily in these selections.
Pro Tips.
Sign up today and get access to the best football betting tips available!
You must be 18 years old or over to use this site. Please bet responsibly.


Why Our Correct Score Tips?
Correct Score is a bet type that highlights the exact score of the outcome of a given match in soccer. Most bookmakers present it in many forms with the most being fixed to first half or full time. It usually one of he most crucial bet to predict but our Experts got you covered.
Top Tips Today.
Table below shows top most correct score tips and predictions for today.
Time League Match Pick 02:05 Colombia 02:05 Colombia - Primera A, Apertura, Round 4 Alianza Petrolera vs Rionegro Aguilas Doradas Correct Score HT(1-0) 04:00 Costa Rica 04:00 Costa Rica - Primera Division, Clausura, Round 6 San Carlos vs CS Cartagines Correct Score HT(1-0) 13:00 Turkey 13:00 Turkey - TFF 3. Lig, Grup 1, Round 17 Bucaspor 1928 vs Diyarbekirspor Correct Score FT(2-1) 14:30 Iran 14:30 Iran - Persian Gulf Pro League, Round 14 Tractor vs Persepolis Correct Score FT(0-1) 16:00 Uruguay 16:00 Uruguay - Primera DivisiГіn, Clausura, Round 4 Club Atletico River Plate vs Danubio Correct Score HT(1-0) 18:00 Ghana 18:00 Ghana - Premier League, Round 12 Medeama vs Accra Hearts of Oak Correct Score HT(0-0) 19:15 United Arab Emirates 19:15 United Arab Emirates - UAE Pro League, Round 15 Al-Wahda FC vs Al-Sharjah Correct Score FT(1-2) 19:30 Italy 19:30 Italy - Serie C, Girone B, Round 22 Mantova vs Padova Correct Score HT(0-1) 20:00 Guatemala 20:00 Guatemala - Liga Nacional, Apertura Playoffs, Final Deportivo Guastatoya vs CSD Municipal Correct Score FT(1-1) 20:30 World 20:30 World - Club World Cup, Round 2 Al Duhail vs Al Ahly Correct Score HT(0-0)
Its is usually abbreviated as "CS" and its annotation is as follows; CS HT(0-0), CS FT(2-1), CS HT(1-0), CS FT(1-1) etc. Most bookmakers have a huge number of odds placed on this bet type, hence our free tips helps your to increase your chances of winning on this bet.
Tomorrow's Tips.
All correct score prediction of the matches scheduled to play tomorrow.
The correct score matches will be updated soon. Otherwise subscribe to our Expert tips or Premium tips.
Best market for correct score tips include Egypt soccer competition, Greece Super League, Turkish Super Lig just to name a few. This is not always guaranteed due to ever changing nature of events. Use our exact score tips to boost your chances of winning.
Yesterday's Tips.
Outcome of correct score predictions yesterday.


Correct Score Football Predictions.
All of our football score predictions can be used to place lucrative wagers on any sports event you find interesting. Here you’ll find a list of correct score betting tips to ensure that your chances of winning are as high as ever. Check out our tips right away to make a sound decision.
What Every Punter Should Know about Correct Score Betting.
The most important advantage of correct scores in football betting is that they are associated with really fantastic odds of winning, especially when using betting predictions provided on our website. It is a highly popular way of placing wagers on your favorite football matches. Besides, it is fairly easy to understand what you need to do to get your winnings. Basically, you’ll need the following:
Check out the score predictions to find out which bet is more likely to be successful; Choose the correct score; If the given score appears to be right at the end of the sports event, you’ll be a winner; If you are dealing with football events where extra time can be taken by the participating teams, the bets will end by the time 90 minutes have passed coupled with injury time; Keep in mind that extra time does not make any difference and is often counted in a totally different market.
Why Keep Track of Correct Score Tips?
There are many reasons why bettors select this betting market. Here are some of the most prominent ones:
Potential winnings. No matter whether you are only a beginner in the betting industry or a real professional, correct score betting remains very popular with punters of all kinds. The key reason is that you can win big-sized rewards because this bet type is probably the most difficult bet to predict in the right way. As such, if you actually get it right, the earnings will be significant. What’s more, the odds of winning are not bad being a lot higher than those offered by other similar betting markets; Exciting and thrilling experience. Correct score betting is perceived by many bookmakers and bettors as one of the most captivating and exciting betting markets existing in today’s industry. What’s even more important, the choice of matches to bet on is really diverse because these bets are available across a wide range of football matches, such as Premier League among many others.
Popular Types of Correct Score Bets.
This betting sector can be divided into different types, each of which has its own rules and demands. Here is what you need to know about them:
Full Time. It’s the most common type which can let you get the greatest returns for the price you pay; Half Time & Full Time. In this bet, you can place bets on either full time or half time separately, even though they may also be combined in which case your potential winnings will grow dramatically; Scorecast. It allows you to bet on the goal scoring outcomes that happen at any time or at the beginning of the match; Extra Time. Any kind of football wager presupposes that your bet will be valid for 90 minutes in total so that you cannot make bets using extra time. However, some bookmakers allow you to predict correct score outcomes when the match comes to an end and whenever extra time is used. Other Results. These are also possible. Some bookmakers allow placing bets together with other results of the match, especially if we are talking about some wide-scale football games. As a result, it can maximize your winnings and make the betting process even more exciting and pleasurable.
How Precise Are the Predictions on Our Site?
No matter how many rewards are at stake, correct score prediction is perhaps the hardest wager to predict. This is the key reason why many bookmakers offer high odds to those bettors who want to give it a try. Our professional tipsters are doing their best to select only those games where the correct score can be predicted in almost 100% of cases. We are trying to avoid football games in which the identity of a future winner cannot be predicted at all, let alone choosing what the actual score will be. As such, we are using all our football-related expertise and deep knowledge of this industry segment in order to provide you with the most trusted tips and predictions and to make them as accurate and precise as possible.


Betting Detectives.
Your Daily Dose Of Betting Tips.
Correct Score Tips.
We are chasing some high-odds correct scores today.
Correct Score Double for Wednesday, 3rd February:
Strasbourg v Brest – 2:0 FT @ 9.00.
Metz v Montpellier – 1:1 FT @ 6.00.
Correct Score Double for Tuesday, 2nd February:
Newcastle v Crystal Palace – 1:1 FT @ 6.50 – LOST.
Rizespor v Kasimpasa – 1:1 FT @ 6.50 – WON.
Correct Score Double for Monday, 1st February:
Real Betis v Osasuna – 2:1 FT @ 8.50 – LOST.
Odisha v Jamshedpur – 1:1 FT @ 6.00 – LOST.
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Correct Score Tips.
This is the place for dreamers and those who chase big wins. Obviously, the money or the win is anything but guaranteed, but the fun most certainly is. If from time to time we get to win big and hit our correct score double, the entertainment will be even greater.
As you most probably know, correct score is one of the toughest markets to predict. However, the odds are proportionally much higher than those you will find with other markets. Landing a single correct score bet is tough. We are going to chase doubles. That’s how brave we are.
Correct Score Predictions – Big Profits.
Why did we decide to have this feature on the site after all? On quite a decent number of occasions, our detectives have managed to land sick odds doubles over the years of betting. Since the theme of the page itself is football betting, we just could not skip good old correct scores.
Detectives will try and update this section regularly. This means that you can expect daily correct score predictions. There is enough of football action across the globe literally on a daily basis. Since detectives will be analysing most of matches, predicting the most likable scores should not be too big of a challenge .
Best Place to Bet on Correct Score.
Online bookmakers frequently go out with special promotions connected to correct score bets. We will also do our job on this field and make sure to let you in on where to place our correct score double picks to get the most of them. Normally, it’s the massive price boost for certain scores. If these match our predictions, that’s a win-win situation.
Also, you can always check our promotions section and back these “risky” tips using exclusively free bets.
Strategy For Correct Score Predictions.
There is not a 100% win strategy for this type of market. What we can do is make our chances as high as possible. Backing 1-1 or 0-0 draws is of the formulas that have used to work very well for us so far. The main principle is to check teams’ form and their H2H stats. If the sides have the similar quality and if you believe the game will end out in a draw, backing correct score instead of the outcome can actually be much more profitable.
At the end of the day, correct score is a combination of a possibility for a huge profit and fun with minimum investments. It will be very interesting to follow our progress here.
Correct Score Double for Monday, 9th November: 42.00 DOUBLE WON!
Kiel v Hamburger SV – 1:1 FT @ 7.00 – WON.
Girona v Las Palmas – 1:1 FT @ 6.00 – WON.
Correct Score Double for Saturday, 28th November: 39.00 DOUBLE WON!
Augsburg v Freiburg – 1:1 FT @ 6.50 – WON.
Elche v Cadiz – 1:1 FT @ 6.00 – WON.


CORRECT SCORE TIPS.
FOOTBALL CORRECT SCORE TIPS.
Correct Score Football Prediction.
Private And Confidential.
Private And Confidential.
Private And Confidential.
Private And Confidential.
Private And Confidential.
Queretaro FC - Pachuca.
Liga MX Guard1anes Clausura.
Queretaro FC - Pachuca.
Liga MX Guard1anes Clausura.
Queretaro FC - Pachuca.
Liga MX Guard1anes Clausura.
Atletico de San Luis - Tijuana.
Liga MX Guard1anes Clausura.
Atletico de San Luis - Tijuana.
Liga MX Guard1anes Clausura.
Independiente Medellin - Millonarios.
Primera A Apertura.
Atletico de San Luis - Tijuana.
Liga MX Guard1anes Clausura.
Independiente Medellin - Millonarios.
Primera A Apertura.
Athletico Paranaense - Internacional.
Athletico Paranaense - Internacional.
Flamengo - Vasco da Gama.
Coquimbo Unido - Cobresal.
Coquimbo Unido - Cobresal.
Coquimbo Unido - Cobresal.
Coquimbo Unido - Cobresal.
Montevideo City Torque - Defensor Sporting.
Primera Division Clausura.
Montevideo City Torque - Defensor Sporting.
Primera Division Clausura.
Montevideo City Torque - Defensor Sporting.
Primera Division Clausura.
Montevideo City Torque - Defensor Sporting.
Primera Division Clausura.
Tottenham Hotspur - Chelsea.
Tottenham Hotspur - Chelsea.
Tottenham Hotspur - Chelsea.
Plaza Colonia - Cerro.
Primera Division Clausura.
Real Betis - Athletic Bilbao.
Plaza Colonia - Cerro.
Primera Division Clausura.
Tottenham Hotspur - Chelsea.
FC Nordsjaelland - Broendby IF.
Belenenses SAD - FC Porto.
FC Nordsjaelland - Broendby IF.
Santa Fe - Patriotas.
Primera A Apertura.
FC Nordsjaelland - Broendby IF.
Belenenses SAD - FC Porto.
Belenenses SAD - FC Porto.
FC Nordsjaelland - Broendby IF.
Santa Fe - Patriotas.
Primera A Apertura.
CFR Cluj - FC Viitorul Constanta.
CFR Cluj - FC Viitorul Constanta.
CFR Cluj - FC Viitorul Constanta.
Al-Duhail SC - Al Ahly.
FIFA Club World Cup.
Apollon Smirnis - AEK Athens.
Al-Duhail SC - Al Ahly.
FIFA Club World Cup.
Al-Duhail SC - Al Ahly.
FIFA Club World Cup.
Al-Duhail SC - Al Ahly.
FIFA Club World Cup.
Al Nassr FC - Al-Taawoun.
FC Midtjylland - SoenderjyskE.
Enosis Paralimni - Ermis Aradippou.
Enosis Paralimni - Ermis Aradippou.
FC Midtjylland - SoenderjyskE.
Mantova - Calcio Padova.
Mantova - Calcio Padova.
Al-Wahda - Sharjah Cultural Club.
Arabian Gulf League.
Al Ittihad - Al Fateh FC.
Chindia Targoviste - Botosani.
Chindia Targoviste - Botosani.
Chindia Targoviste - Botosani.
Asteras Tripolis - Aris Thessaloniki FC.
Farense - Santa Clara.
Al-Hidd - East Riffa.
Al-Hidd - East Riffa.
Al-Hidd - East Riffa.
Al-Hidd - East Riffa.
Farense - Santa Clara.
Farense - Santa Clara.
Alma Juventus Fano - Carpi.
Tigres - Ulsan Hyundai.
FIFA Club World Cup.
Northeast United FC - FC Goa.
Indian Super League.
Northeast United FC - FC Goa.
Indian Super League.
Alma Juventus Fano - Carpi.
Tigres - Ulsan Hyundai.
FIFA Club World Cup.
Northeast United FC - FC Goa.
Indian Super League.
Tigres - Ulsan Hyundai.
FIFA Club World Cup.
Northeast United FC - FC Goa.
Indian Super League.
Alma Juventus Fano - Carpi.
TRAU - Punjab FC.
TRAU - Punjab FC.
Al Ain - Al-Ettifaq.
Al Ain - Al-Ettifaq.
Goztepe - Yeni Malatyaspor.
River Plate - Danubio.
Primera Division Clausura.
River Plate - Danubio.
Primera Division Clausura.
Goztepe - Yeni Malatyaspor.
OFI Crete - NFC Volos.
OFI Crete - NFC Volos.
Goztepe - Yeni Malatyaspor.
FC Academica Clinceni - UTA Arad.
FC Academica Clinceni - UTA Arad.
FC Academica Clinceni - UTA Arad.
FC Academica Clinceni - UTA Arad.
What is Correct Score betting?
How often do we provide Correct Score tips?
Which football leagues and competitions are in our correct score tips?
Correct score predictions sites.
It is always great for bettors to find correct score football prediction from the most efficient tipsters for soccer betting tips. Soccer betting becomes more exciting when you are winning money. The website shall give the right predictions and tips for soccer games in the correct score market. Betting along with tips is really helpful as it improves the possibilities to win. Buying tips is simple and affordable. It doesn’t cost much It includes lots of attractive offers It’s available instantly and daily It’s available for dozens of matches every day Tipsters can give the correct score prediction of top-rated leagues and matches. They possess the data, insider stories, and the requisite info needed to calculate the winning possibilities of the participating teams. They can say you the accurate score and moreover, they can provide you secret information related to football betting odds. Tipsters can give the correct score prediction of top-rated leagues and matches. They possess the data, insider stories, and the requisite info needed to calculate the winning possibilities of the participating teams. They can say you the accurate score and moreover, they can provide you secret information related to football betting odds.
Correct score prediction for tomorrow.
Correct score prediction is one of the most popular soccer surebets to place but they are the hardest to get them right. Due to this, the odds and the rewards are decent Though this prediction is the hardest bet if you get it correct you can get good odds. It is difficult to predict correctly the exact score of a soccer match, however, there are ways you can improve the possibilities to place successful bets.
The soccer prediction sites study the form of teams, look at games wherein fewer goals might be scored, and look at leagues wherein match outcomes become easier to predict. These websites produce correct score tomorrow using own algorithm along with mathematical algorithm. The calculation considers many factors and variables such as form, attacking and defensive statistics along with the probability of the score lines. For each correct score tomorrow, you shall find updated odds from various bookmakers as returns per stake. From our website, you will get lots of help in several other ways as well.




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DATE MATCH PICK ODD SCORE 06.02.2021 30.01.2021 FC 08 Homburg – Ulm 4:4 276.00 4:4 Win 16.01.2021 Sparta Rotterdam – PSV 3:5 151.00 3:5 Win 09.01.2021 Ypsonas – Sotiras 4:2 101.00 4:2 Win 02.01.2021 Nea Salamis – Doxa 4:3 151.00 4:3 Win 26.12.2020 Lincoln – Burton 5:1 81.00 5:1 Win 19.12.2020 Walsall – Port Vale 4:3 151.00 4:3 Win 12.12.2020 Varazdin – Hajduk Split 4:2 126.00 4:2 Win 05.12.2020 Weymouth – FC Halifax 1:5 101.00 1:5 Win 28.11.2020 St. Polten – Salzburg 2:8 151.00 2:8 Win 21.11.2020 Salzburg – Sturm Graz 1:3 151.00 1:3 Win 14.11.2020 Port Vale – Tranmere 3:4 201.00 3:4 Win 07.11.2020 Cavese – Ternana 0:5 81.00 0:5 Win 24.10.2020 Tonbridge – Taunton 5:0 101.00 5:0 Win 10.10.2020 KPV Kokkola – AC Kajaani 8:2 201.00 8:2 Win 03.10.2020 Domazlice – Plzen B 8:0 101.00 8:0 Win 26.09.2020 Inter – Fiorentina 4:3 101.00 4:3 Win 19.09.2020 Leeds – Fulham 4:3 126.00 4:3 Win 12.09.2020 Fanna – Kungsangens 5:4 201.00 5:4 Win 05.09.2020 Libertad Asuncion – Guairena FC 5:2 151.00 5:2 Win 29.08.2020 Frolunda – Varbergs GIF 5:0 91.00 5:0 Win 22.08.2020 Vilniaus Vytis – FK Panevezys 2 0:6 201.00 0:6 Win 15.08.2020 Mladost DK – Tuzla City 2:5 101.00 2:5 Win 08.08.2020 Detmarovice – Novy Jicin 6:0 101.00 6:0 Win 01.08.2020 Kolin – Hradec Kralove B 8:0 151.00 8:0 Win 25.07.2020 Boston Utd – Gateshead 5:3 101.00 5:3 Win 18.07.2020 Dornbirn – Horn 5:2 201.00 5:2 Win 11.07.2020 BumProm Gomel – Gorki 0:6 251.00 0:6 Win 04.07.2020 SMIavtotrans – Zhlobin 10:1 101.00 10:1 Win 27.06.2020 Skra – Gryf Wejherowo 7:0 131.00 7:0 Win 20.06.2020 KR Reykjavik – Kopavogur 0:3 126.00 0:3 Win.
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Make 24.
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Math Clash.
Calculate This!
Sum Blocks.
Ninenin.
Swap Sums.
Sum Links 2.
Chain Sums.
Plus 10.
Sum Tracks.
Sum Points.
Make 15.
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Crunch numbers to collect coins.
Sum Blocks.
Use the overlapping area to hit the target sums!
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Everything must add up to 9!
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Click a number, then an operator, and then another number to perform the calcuation. Use all four cards to make 24 and earn the gold card.
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Tap a number, then an operator, and then another number to perform the calculation. Use all four cards to make 24 and earn the gold card.
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Tap a number, then an operator, and then another number to perform the calculation. Use all four cards to make 24 and earn the gold card.
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Calculate This!
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Swap Sums.
Sum Links 2.
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Sum Points.
Make 15.
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Fixed Matches Half Time Full Time ARCHIVE.
Fixed Matches Half Time Full Time ARCHIVE.
Date Match Tip Odds HT – FT 04.01.2020 Red Star – Chambly 2/1 29.00 0:1 – 2:1 WIN 11.01.2020 Bordeaux – Lyon 1/2 29.00 1:0 – 1:2 WIN 18.01.2020 Sassuolo – Torino 2/1 26.00 0:1 – 2:1 WIN 25.01.2020 Crawley – Grimsby 2/1 29.00 1:2 – 3:2 WIN 01.02.2020 Meppen – Chemnitzer 1/2 34.00 1:0 – 1:2 WIN 08.02.2020 Metz – Bordeaux 1/2 34.00 1:0 – 1:2 WIN 15.02.2020 Lahti – Rovaniemi 2/1 23.00 0:1 – 2:1 WIN 22.02.2020 Albacete – Numancia 2/1 34.00 0:1 – 2:1 WIN 29.02.2020 Wolfsburg II – Lubeck 1/2 26.00 1:0 – 1:2 WIN 07.03.2020 DC United – Inter Miami CF 2/1 23.00 0:1 – 2:1 WIN 14.03.2020 Bani Yas – Ittihad Kalba 2/1 26.00 0:1 – 2:1 WIN 10.05.2020 Mikashevichi – Volna Pinsk X/2 6.00 0:0- 0:1 WIN 16.05.2020 Opava (Cze) – Vitkovice (Cze) 2/1 21.00 0:1 – 4:1 WIN 23.05.2020 Kalju – Legion 2/2 15.00 0:1 – 1:2 WIN 30.05.2020 Slaven Belupo – Lokomotiva Zagreb 1/2 26.00 1:0 – 1:3 WIN 06.06.2020 Spartak Subotica – Partizan 2/1 41.00 1:2 – 3:2 WIN 13.06.2020 Horn – Amstetten 1/2 23.00 1:0 – 1:2 WIN 21.06.2020 Skovde AIK – Landskrona 1/2 23.00 2:0 – 2:3 WIN 27.06.2020 Mattersburg – Admira 1/2 29.00 1:0 – 1:2 WIN 04.07.2020 Spartak Moscow – Tambov 1/2 41.00 1:0 – 2:3 WIN 11.07.2020 Stomil Olsztyn – Legnica 1/2 34.00 1:0 – 1:2 WIN 18.07.2020 Wil – Vaduz 1/2 23.00 1:0 – 1:2 WIN 25.07.2020 Poblense – Mallorca B 2/1 23.00 0:1 – 2:1 WIN 01.08.2020 Incheon – Gwangju FC 1/2 29.00 1:0 – 1:3 WIN 08.08.2020 Daejeon – Gyeongnam 1/2 34.00 2:0 – 2:3 WIN 15.08.2020 Leiknir F. – Grindavik 2/1 36.00 0:1 – 4:3 WIN 22.08.2020 Seekirchen – Saalfelden 2/1 26.00 0:1 – 2:1 WIN 29.08.2020 Suwon Bluewings – Busan 2/1 29.00 0:1 – 3:1 WIN 05.09.2020 AFC Totton (Eng) – Poole (Eng) 2/1 29.00 0:1 – 2:1 WIN 12.09.2020 Cheltenham – Morecambe 1/2 41.00 1:0 – 1:2 WIN 19.09.2020 Tacuarembo – Sud America 1/2 30.00 1:0 – 1:2 WIN 26.09.2020 Covilha – Varzim 2/1 26.00 0:1 – 2:1 WIN 03.10.2020 Woking – Solihull Moors 2/1 34.00 0:1 – 2:1 WIN 10.10.2020 Bromley – Torquay 1/2 32.00 1:0 – 1:2 WIN 17.10.2020 Oygarden – Strommen 1/2 23.00 1:0 – 1:4 WIN 24.10.2020 Vizela – Academica 1/2 34.00 1:0 – 1:3 WIN 31.10.2020 Suzhou Dongwu – Jiangxi Liansheng 2/1 29.00 0:1 – 2:1 WIN 07.11.2020 Ceske Budejovice – Zlin 1/2 34.00 1:0 – 1:2 WIN 14.11.2020 Amorebieta – R. Sociedad B 2/1 31.00 0:1 – 2:1 WIN 21.11.2020 Carlos Stein – Binacional 1/2 26.00 1:0 – 1:3 WIN 28.11.2020 Villa Espanola – Tacuarembo 2/1 29.00 0:1 – 3:2 WIN 05.12.2020 Plymouth – Ipswich 1/2 29.00 1:0 – 1:2 WIN 12.12.2020 Albion Rovers – Elgin 2/1 41.00 0:1 – 3:1 WIN 19.12.2020 Alloa – Inverness 2/1 29.00 0:1 – 2:1 WIN 26.12.2020 Cambridge Utd – Leyton Orient 2/1 26.00 0:1 – 2:1 WIN 02.01.2021 Helmond – Dordrecht 2/1 23.00 1:2 – 5:2 WIN 09.01.2021 Gloucester – Kidderminster 2/1 26.00 1:2 – 3:2 WIN 17.01.2021 Yeni Malatyaspor – Rizespor 2/1 23.00 0:1 – 4:1 WIN 23.01.2021 Tarazona – Izarra 2/1 23.00 0:1 – 2:1 WIN 30.01.2021 Ascoli – Brescia 2/1 29.00 0:1 – 2:1 WIN.
Fixed Matches Half Time Full Time ARCHIVE.
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п»їWelcome to Fixed Football Matches.
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SUBSCRIPTION ARCHIVE FEBRUARY 2021.
01.02.2021.
odd: 7.59.
Sumqayit-Sabail.
Hallescher-Saarbrucken.
1.
X.
2.30.
3.30.
0-1 LOSE.
1-1 WIN.
02.02.2021.
odd: 5.00.
Sheffield Utd-West Brom.
Charlton-Portsmouth.
1.
2.
2.00.
2.50.
2-1 WIN.
1-3 WIN.
SUBSCRIPTION ARCHIVE JANUARY 2021.
01.01.2021.
odd: 11.55.
Everton-West Ham.
Sheffield Wed-Derby.
2.
1.
3.30.
3.50.
0-1 WIN.
1-0 WIN.
02.01.2021.
odd: 9.90.
Brighton-Wolves.
FC Koln-Augsburg.
X.
2.
3.00.
3.30.
3-3 WIN.
0-1 WIN.
03.01.2021.
odd: 9.60.
Genoa-Lazio.
Eibar-Granada CF.
X.
1.
4.00.
2.40.
1-1 WIN.
2-0 WIN.
04.01.2021.
odd: 4.60.
Dusseldorf-Paderborn.
Khooshe Talaee-Navad Urmia.
1.
1.
2.30.
2.00.
2-1 WIN.
1-0 WIN.
05.01.2021.
odd: 5.75.
Ermis-Nea Salamis.
Chamby-Le Hevre.
2.
2.
2.30.
2.50.
1-2 WIN.
0-1 WIN.
06.01.2021.
odd: 7.36.
Brest-Nice.
Bologna-Udinese.
1.
X.
2.30.
3.20.
2-0 WIN.
2-2 WIN.
07.01.2021.
odd: 9.72.
Coritiba-Goias.
Al Ittihad-El Masry.
2.
X.
3.60.
2.70.
1-2 WIN.
1-1 WIN.
08.01.2021.
odd: 8.40.
Celta Vigo-Villarreal.
Sandhausen-Heidenheim.
2.
1.
2.10.
4.00.
0-4 WIN.
4-0 WIN.
09.01.2021.
odd: 8.96.
Metz-Nice.
RB Leipzig-Dortmund.
X.
2.
3.20.
2.80.
1-1 WIN.
1-3 WIN.
10.01.2021.
odd: 10.92.
Ajax-PSV.
Colo Colo-Everton.
X.
1.
4.20.
2.60.
2-2 WIN.
1-0 WIN.
11.01.2021.
odd: 6.72.
Spezia-Sampdora.
Vasco-Botafogo RJ.
1.
1.
3.20.
2.10.
2-1 WIN.
3-0 WIN.
12.01.2021.
odd: 12.54.
Wolves-Everton.
Oostende-Eupen.
2.
X.
3.30.
3.80.
1-2 WIN.
1-1 WIN.
13.01.2021.
odd: 7.75.
Den Haag-Venlo.
Avai-Vitoria.
2.
X.
2.50.
3.10.
1-4 WIN.
2-2 WIN.
14.01.2021.
odd: 4.40.
Willem II-Groningen.
Corinthians-Fluminense.
2.
1.
2.20.
2.00.
2-3 WIN.
5-0 WIN.
15.01.2021.
odd: 7.92.
Montepllier-Monaco.
U. Espanola-U. Catolica.
2.
X.
2.20.
3.60.
2-3 WIN.
1-1 WIN.
16.01.2021.
odd: 7.48.
Bologna-Verona.
Magra U21-Paradou U21.
1.
X.
2.20.
3.40.
1-0 WIN.
1-1 WIN.
17.01.2021.
odd: 9.12.
Liverpool-Manchester Utd.
Inter-Juventus.
X.
1.
3.80.
2.40.
0-0 WIN.
2-0 WIN.
18.01.2021.
odd: 7.82.
Deportes Tolima-Once Caldas.
Millwall U23-Ipswich U23.
X.
2.
3.40.
2.30.
1-1 WIN.
0-1 WIN.
19.01.2021.
odd: 5.52.
Zambia-Tanzania.
Avai-Juventude.
1.
1.
2.40.
2.30.
2-0 WIN.
5-2 WIN.
20.01.2021.
odd: 5.04.
Schalke-FC Koln.
Betis-Celta Vigo.
2.
1.
2.40.
2.10.
1-2 WIN.
2-1 WIN.
21.01.2021.
odd: 7.44.
Partizani-KF Tirana.
Coritiba-Fluminense.
1.
X.
2.40.
3.10.
1-0 WIN.
3-3 WIN.
22.01.2021.
odd: 11.88.
Benevento-Torino.
Uganda-Togo.
X.
2.
3.30.
3.60.
2-2 WIN.
1-2 WIN.
23.01.2021.
odd: 4.84.
AC Milan-Atalanta.
Namibia-Tanzania.
2.
2.
2.20.
2.20.
0-3 WIN.
0-1 WIN.
24.01.2021.
odd: 6.50.
Bordeaux-Angers.
Osasuna-Granada CF.
1.
1.
2.50.
2.60.
2-1 WIN.
3-1 WIN.
25.01.2021.
odd: 6.67.
Ath Bilbao-Getafe.
Teuta-Partizani.
1.
X.
2.30.
2.90.
5-1 WIN.
1-1 WIN.
26.01.2021.
odd: 5.06.
Crystal Palace-West Ham.
Rukinzo-Olympic S.
2.
1.
2.20.
2.30.
2-3 WIN.
1-0 WIN.
27.01.2021.
odd: 7.48.
Brighton-Fulham.
Lorient-Dijon.
X.
1.
3.40.
2.20.
0-0 WIN.
3-2 WIN.
28.01.2021.
odd: 7.54.
Atl.Rafaela-Platense.
CRAC-Atletico GO.
2.
1.
2.70.
2.60.
0-2 WIN.
5-3 WIN.
29.01.2021.
odd: 6.93.
Torino-Fiorentina.
Lommel SK-Deinze.
X.
1.
3.30.
2.10.
1-1 WIN.
4-2 WIN.
30.01.2021.
odd: 6.38.
Crystal Palace-Wolves.
Heracles-Groningen.
1.
1.
2.90.
2.20.
1-0 WIN.
1-0 WIN.
31.01.2021.
odd: 7.20.
Cagliari-Sassuolo.
West Ham-Liverpool.
X.
2.
3.60.
2.00.
1-1 WIN.
1-3 WIN.
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LAST VIP COMBO TICKET 30.01.2021 ( Saturday ) TOTAL ODD : 300.00.
PROOF 30.01.2021 :
DATE HOME VS AWAY TIP ODD FULL TIME 30.01.2021 West Brom – Fulham X 3.30 2-2 WIN 30.01.2021 Shrewsbury – Peterborough 1 3.10 2-0 WIN 30.01.2021 Grimsby – Stevenage 2 2.80 1-2 WIN 30.01.2021 Nancy – Paris FC X 3.20 1-1 WIN 30.01.2021 Oberhausen – Rodinghausen 2 3.20 2-3 WIN.
LAST VIP COMBO TICKET 09.01.2021 ( Saturday ) TOTAL ODD : 420.00.
PROOF 09.01.2021 :
DATE HOME VS AWAY TIP ODD FULL TIME 09.01.2021 Rennes – Lyon X 3.75 2-2 WIN 09.01.2021 Waalwijk – Den Haag 2 4.20 0-1 WIN 09.01.2021 Kortrijk – Genk 1 3.40 2-1 WIN 09.01.2021 AEL Limassol – Omonia X 3.40 1-1 WIN 09.01.2021 Cambridge Utd – Harrogate 1 2.30 2-1 WIN.
LAST VIP COMBO TICKET 26.12.2020 ( Saturday ) TOTAL ODD : 700.00.


03/FEB/2020.
Odds&nbsp 2.20.
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Looking for the information on the good prediction site that takes the lead for real football prediction?
We’ve got the list of the best 10.
How often do you visit any of the football predictions websites in Nigeria, for best football bet tips and predictions, before placing your bets?
Can you actually tell the outcome of a football match without a soccer score predictor? I mean football draw predictions?
Best soccer predictions for today is what many look for to make the best decisions for each day’s match, who loves to lose? No one.
In this updated post about the best football prediction sites in Nigeria , you’ll find out why and how each of these platforms for real football prediction stack up.
You’ll find information about platforms that provides Free Soccer and Accurate Football predictions, and most recommended tips prediction for daily best Leagues to bet on, odds banker, football stakes and more.
At the end of this piece, you’ll be informed about the best soccer prediction site that predicts sports outcomes, accurately.
List of 14 Good Prediction Site That Predict Football Matches Correctly.
NOTE: This list was compiled based on Alexa ranking as at the 2 nd of January 10th, 2020.
This online website gives bettors football tips to aid soccer prediction that will give them an advantage over other bettors at the betting centre or on any of the football betting sites.
Accurate soccer prediction that earns you more victories than losses is definitely guaranteed on this site.
Accord to Alexa ranking, it is the 127 th site in Nigeria but the 1 st of the best football prediction sites. How about checking it out?
Ranking 543 rd site in Nigeria and 2 nd on this list of good football prediction sites that have got free soccer predictions and free football tips on the menu.
This site is one of the football prediction websites that have experts using detailed analysis, betting tips, etc. to give accurate soccer prediction.
VictorPredict is a free football predictions website with an ultra-modern platform ranking 678th site in Nigeria, based on Alexa rankings.
VictorsPredict is an online service that provides free football tips and predictions for football fans around the world, free analysis, latest results, league tables and many more.
On their website, you’ll find upcoming picks, latest winnings, double chance, sure 2 odds, draws, super singles, BTT/GG in both their basic and silver plans.
Are you confused with picking either over 2.5, BTTS, under 2.5, or single bet? Say no more. Confirm Bets ranks 978 th website in Nigeria and 4th on my list of top soccer prediction sites.
Confirmbets has got gurus to help you make better decision by providing reliable and profitable tips on one of the top football prediction site.
Stakegains has also made it to the list of good prediction sites in Nigeria, ranking 1262 nd website in Nigeria and the 5 th on my list.
It is one of the rare soccer prediction sites that give evergreen predictions for sure games . They also claim to have the best soccer prediction App.
It is not a football betting site but has got sure wins football predictions, made by football maestros, with a minimum of 80% accuracy.
I have not seen what has attracted more consistent cheers than a football match ; even teachers wish male students especially would place their subjects above the names of the top 10 richest footballers in the world .
I, for one, have not seen seriousness so serious on the face of a bettor when he is watching a match…you would not blame the uneasiness, right?
But what if he knew there were football prediction sites? Do you think he will smile a little or relax more during the match?
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You’ve definitely seen the top 5. Still, the following hot football prediction sites are a game-changer. Check the list out.
6. Oddslot Soccer Prediction Website.
Oddslot is one of the good prediction sites in Nigeria that offers free soccer predictions and daily soccer betting tips.
It is the 1427 th site in Nigeria and the 6th on my list of top best football prediction sites.
7. Forecastr.
Ranking 1461 st site in Nigeria and owning the 7 th position on my list of best prediction sites in Nigeria.
Forecastr is one big hot prediction site that gives you an unlimited list of soccer predictions but here is the thing, you need to subscribe to see this exclusive list.
8. Score Predictor.
As it name implies, it predicts either BTTS, over 2.5, under 2.5, single bet, etc for a football match.
It is one of the football prediction websites that is not limited to soccer prediction for today or tomorrow but also involved in basketball and handball.
It is the 8 th best football prediction site as it ranks the 1325 th site in Nigeria.
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9. Sport Verified.
Looking for verification of the match prediction you thought of? You are actually at the right bus stop.
Not excluded from the list of good prediction site in Nigeria, it guides punters on football predictions and betting.
This soccer prediction site supplies “the best and most accurate soccer prediction anytime, any day” in a unique way.
10. Betloy.
It is one of soccer prediction sites that guarantee victory over the bookies with the best football tips.
Betloy is one accurate prediction site that forecasts for football match prediction banker, with its 100% free football match predictions.
Its service was said to be rated the “best football prediction site of the year”. Nevertheless, it is the 2194 th site in Nigeria and ranks 9 th on this list of best sports prediction sites.
11. Betshoot.
Betshoot is another football prediction website in Nigeria that ranks 2438 th site in the country, hence, bagging the 10 th position on this list of good football prediction sites.
12. Fcpredicts.
On the 11 th position of all good prediction site, FCPredicts assures you of accurate football prediction as their motto is “winning is our priority”.
13. 1960Tips.
When you join this football prediction website, you are not just talking football predictions or football tips to earn you more cash, you will also be making extra through referral.
14. Betpera.
Betpera is the last on the list of top good soccer prediction sites but the 3583 rd site in Nigeria.
BETENSURED is not just one of the top football prediction sites but an accurate football prediction website that gives guidance to the risk-takers on the journey they are about to embark on, betting.
The Best Football Prediction Site of the Year.
Follow any of the links above; I can assure you that they rate themselves as the best football prediction site of the year.
I try small, ko? But tell me, which is actually the best football prediction site of the year?
Is it still betloy. abeg o, make una tell me I no wan drink garri again. You know what, let’s have your opinion and reasons in the comment section below this post.
To get the best football tips of the day, visit any of the top 5 best prediction websites listed in this article. Other sites listed also provide football tips of the day.
Gambling , they once said and still say, is bad mainly because of the consequences of its addiction but despite all talks, whoops of victory, and/or cries of defeat of a gambling game; betting is as old as time.
The word “ betting ” has become an ancestor, a great-great-great-grandparent to games, with football at the centre of it all.
The bad-mouthing of gambling’s entire being in itself has not stopped the birth of many of the football prediction sites and top football betting sites alongside .
There is a great difference between watching a match and watching a match after placing a bet ; the adrenaline and alertness of the sense organs; your reaction to the way the ball moves is entirely different.
Football betting is fun especially when you are raking in your cash but heart-wrenching painful when you lose especially without a point in the right direction.
I know how it feels to lose everything and wish you had half; that day I understood the saying “half bread is better than none”.
The agony of losing the game that weekend is an ever-fresh memory; a game spoilt the entire success. Damn! That was my last cash. I drank my garri o.
I had called my guru friend to ask her, what bet to place; alas! It was not as reliable as the last.
I don’t really watch football matches and know so little about football except for Barca Messi, Juve C.Ronaldo, and Madrid’s Marcelo but I jump at an opportunity to earn more when I see an opportunity. I heard you, ma sun (stay awake) abi.
No be only me waka come for this one, if not all of us, some of us have that friend that can qualify for one of the good prediction sites in Nigeria that make you count cash instead of biting nails.
Good Prediction Site In Nigeria Summary.
Football has got a special bond with mostly men than the women… (I’m not trying to be chauvinistic here, just stating facts from my end here)… we love football, right?
Why not make money, while loving what we love, using everything at our disposal from watching football matches ourselves over time.
Interestingly, you can now do it easily with football tips from any of the good prediction site in Nigeria , and place bets on any of the sports betting sites.
Here’s a quick recap once more:
Betensured Tips180 Soccer Prediction Victor Predict Confirmbets Stakegains Oddslot Soccer Prediction Score Predictor Sport Verified Betloy Betshoot Fc Predicts 1960Tips BetPera.
Found this post helpful and engaging? I can’t wait to have your comment.




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Date: 03/ 02/2021 (WEDNESDAY )
EGYPT Premier League.
18:00 Odense – Lyngby OVER 2.5 (2.00)
02/ 02/2021 (TUESDAY )
20:00 Le Havre – Nancy X (DRAW) (3.00)
Date: 01/ 02/2021 (MONDAY )
21:00 Reggina – Salernitana X (DRAW) (3.30)


Norway Source.
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VIP Ticket for 29.08.2020.
DATE MATCH TIP ODD RESULT W/L 29.08.2020 – ?? ?? ?? ?? 29.08.2020 – ?? ?? ?? ?? 29.08.2020 – ?? ?? ?? ?? 29.08.2020 – ?? ?? ?? ??
VIP Ticket for 22.08.2020.
DATE MATCH TIP ODD RESULT W/L 22.08.2020 Lille – Rennes 1/X 17.00 1:0/1:1 WIN 22.08.2020 Dijon – Angers 2 2.35 0:1 WIN 22.08.2020 Antwerp – Gent 1 3.00 1:0 WIN 22.08.2020 Atletico-PR – Fluminense 2 4.50 0:1 WIN.
VIP Ticket for 15.08.2020.
DATE MATCH TIP ODD RESULT W/L 15.08.2020 Manchester City – Lyon 2/2 21.00 0:1/1:3 WIN 15.08.2020 Osijek – Slaven Belupo X 3.00 0:0 WIN 15.08.2020 Sydney FC – Western United 2 4.00 1:2 WIN 15.08.2020 Botafogo SP – Guarani 2 2.45 0:1 WIN.
VIP Ticket for 08.08.2020.
DATE MATCH TIP ODD RESULT W/L 08.08.2020 Club Brugge – Charleroi X/2 16.00 0:0/0:1 WIN 08.08.2020 Busaiteen – East Riffa X 3.40 2:2 WIN 08.08.2020 Velez Mostar – Zrinjski 1 2.50 2:0 WIN 08.08.2020 Valerenga – Bodo/Glimt X 4.00 2:2 WIN.
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DATE MATCH TIP ODD RESULT W/L 18.07.2020 Famalicao – Boavista 1/X 15.00 2:0/2:2 WIN 18.07.2020 Baerum Sportsklubb – Levanger X 3.60 0:0 WIN 18.07.2020 Norwich – Burnley 2 2.30 0:2 WIN 18.07.2020 Blackburn – Reading 1 2.10 4:3 WIN.
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August 2020.
DATE MATCH TIP ODDS RES W/L 01.08.2020 Napoli – Lazio 1 3.25 3:1 WIN 02.08.2020 Lecce – Parma 2 5.25 3:4 WIN 03.08.2020 Asane – Tromso 2 2.30 0:1 WIN 04.08.2020 Brentford – Fulham X 3.80 0:0 WIN 05.08.2020 Shakhtar Donetsk – Wolfsburg 1 3.10 3:0 WIN 06.08.2020 Basel – Eintracht Frankfurt 1 3.80 1:0 WIN 07.08.2020 Vitebsk – Isloch 2 3.60 2:3 WIN 08.08.2020 Levanger – Nardo X 4.00 0:0 WIN 09.08.2020 Rockdale City Suns – Marconi Stallions 1 3.80 1:0 WIN 10.08.2020 Honka – SJK X 3.80 0:0 WIN 11.08.2020 Široki Brijeg – Velež Mostar X 3.50 2:2 WIN 12.08.2020 Parana – Avai 1 3.20 1:0 WIN 13.08.2020 RB Leipzig – Atl. Madrid 1 3.50 2:1 WIN 14.08.2020 Varaždin – Gorica 2 2.60 1:5 WIN 15.08.2020 America MG – Operario X 3.10 1:1 WIN 16.08.2020 Sevilla – Manchester Utd 1 2.90 2:1 WIN 17.08.2020 Enppi – Smouha X 2.50 0:0 WIN 18.08.2020 Juventude – America MG X 3.00 0:0 WIN 19.08.2020 Din.Tibilisi – Tirana 2 7.00 0:2 WIN 20.08.2020 Al Hala – Busaiteen 1 3.10 2:1 WIN 21.08.2020 Sevilla – Inter 1 3.30 3:2 WIN 22.08.2020 Lille – Rennes X 3.40 1:1 WIN 23.08.2020 MAINTENANCE 24.08.2020 MAINTENANCE 25.08.2020 MAINTENANCE 26.08.2020 MAINTENANCE 27.08.2020 Sumqayit -Shkendija 2 2.50 0:2 WIN 28.08.2020 – ?? ?? ?? ??
FREE DAILY FOOTBALL TIPS.
Start: 28 August 2020 / 21:00 h.
FRANCE:Ligue 1.
Lyon – Dijon.
Tip : 1 ( Home Team To Win ) Odds : 1.40 .
FREE DAILY FOOTBALL TIPS.
Start: 17 August 2020 / 21:00 h.
EUROPE:Europa League.
Inter – Shakthar.
Tip : 1 ( Home Team To Win ) Odds : 1.80.
Result: 5:0 Win.
FREE DAILY FOOTBALL TIPS.
Start: 16 August 2020 / 21:00 h.
EUROPE:Europa League.
Sevilla – Manchester Utd.
Tip : BTS ( Both Teams Score ) Odds : 1.90.
Result: 2:1 Win.
FREE DAILY FOOTBALL TIPS.
Start: 15 August 2020 / 21:00 h.
EUROPE:Champions League.
Manchester City – Lyon.
Result: 1:3 Win.
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Start: 14 August 2020 / 21:00 h.
EUROPE:Champions League.
Barcelona – Bayern.
Result: 2:8 Win.
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Start: 13 August 2020 / 21:00 h.
EUROPE:Champions League.
RB Leipzig – Atl. Madrid.
Tip : BTS ( Both Teams To Score ) Odds : 1.90.
Result: 2:1 Win.
FREE DAILY FOOTBALL TIPS.
Start: 12 August 2020 / 21:00 h.
EUROPE:Champions League.
Atalanta – PSG.
Tip : BTS ( Both Teams To Score ) Odds : 1.80.
Result: 1:2 Win.
FREE DAILY FOOTBALL TIPS.
Start: 11 August 2020 / 20:45 h.
IRELAND:FAI Cup.
Dundalk-Waterford.
Tip : 1 ( Home To Win ) Odds : 1.35.
Result: 1:0 Win.
FREE DAILY FOOTBALL TIPS.
Start: 09 August 2020 / 21:00 h.
SERBIA:Super League.
Proleter – Backa.
Tip : 1 ( Home To Win ) Odds : 1.72.
Result: 1:1 Lose.
FREE DAILY FOOTBALL TIPS.
Start: 08 August 2020 / 21:00 h.
EUROPE: Champions League.
Barcelona – Napoli.
Tip : 1 ( Home To Win ) Odds : 1.61.
Result: 3:1 Win.
FREE DAILY FOOTBALL TIPS.
Start: 07 August 2020 / 21:00 h.
EUROPE: Champions League.
Juventus – Lyon.
Tip : 1 ( Home To Win ) Odds : 1.53.
Result: 2:1 Win.
FREE DAILY FOOTBALL TIPS.
Start: 06 August 2020 / 21:00 h.
EUROPE: Europa League – Play Offs.
Basel – Eintracht Frankfurt.
Result: 1:0 Lose.
FREE DAILY FOOTBALL TIPS.
Start: 05 August 2020 / 19:00 h.
SERBIA: Super Liga.
FK Crvena zvezda – Radnicki Nis.
Tip : 1/1 ( HT / FT ) Odds : 1.80.
Result: 2:0 / 3:0 Win.
FREE DAILY FOOTBALL TIPS.
Start: 03 August 2020 / 19:00 h.
SWEDEN: Allsvenskan.
Norrkoping – Mjallby.
Tip : 1 ( Home To Win ) Odds : 1.50.
Result: 1:1 Lose.
FREE DAILY FOOTBALL TIPS.
Start: 02 August 2020 / 20:45 h.
ITALY: Serie A.
Genoa – Verona.
Tip : 1 ( Home To Win ) Odds : 1.65.
Result: 3:0 Win.
FREE DAILY FOOTBALL TIPS.
Start: 01 August 2020 / 16:00 h.
NORWAY: Division 2.
Fredrikstad – Skeid.
Tip : 1 ( Home To Win ) Odds : 1.60.
Result: 2:1 Win.
FREE DAILY FOOTBALL TIPS.
Start: 31 July 2020 / 20:00 h.
SERBIA: Super Liga.
Mladost – Javor.
Tip : 1 ( Home To Win ) Odds : 1.57.
Result: 2:1 Win.
FREE DAILY FOOTBALL TIPS.
Start: 30 July 2020 / 23:00 h.
BRAZIL: Campeonato Catarinense.
Brusque – Joinville.
Tip : 1 ( Home To Win ) Odds : 1.53.
Result: 2:1 Win.
FREE DAILY FOOTBALL TIPS.
Start: 29 July 2020 / 19:30 h.
ITALY: Serie A.
Verona – Spal.
Tip : 1 ( Home To Win ) Odds : 1.55.
Result: 3:0 Win.
FREE DAILY FOOTBALL TIPS.
Start: 28 July 2020 / 20:30 h.
ROMANIA: Liga 1.
CFR Cluj – FC Botosani.
Tip : 1 ( Home To Win ) Odds : 1.50.
Result: 1:0 Win.
FREE DAILY FOOTBALL TIPS.
Start: 27 July 2020 / 18:00 h.
NORWAY: OBOS-ligaen.
Tromso – Oygarden.
Result: 1:0 Lose.
FREE DAILY FOOTBALL TIPS.
Start: 25 July 2020 / 17:30 h.
SWEDEN: Superettan.
Halmstad – Akropolis.
Tip : 1 ( Home To Win ) Odds : 1.50.
Result: 2:3 Lose.
FREE DAILY FOOTBALL TIPS.
Start: 23 July 2020 / 19:00 h.
SWEDEN: Allsvenskan.
Malmo FF – Hammarby.
Tip : 1 ( Home To Win ) Odds : 1.75.
Result: 3:0 Win.
FREE DAILY FOOTBALL TIPS.
Start: 22 July 2020 / 20:30 h.
ENGLAND: Championship.
Cardiff – Hull.
Tip : 1 ( Home To Win ) Odds : 1.50.
Result: 3:0 Win.
FREE DAILY FOOTBALL TIPS.
Start: 21 July 2020 / 19:00 h.
LATVIA: Optibet Virsliga.
Riga FC – Ventspils.
Tip : 1 ( Home To Win ) Odds : 1.50.




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