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I've been experimenting with artificial intelligence engines in an attempt to formulate a profitable strategy in the correct scores markets. Beating the market odds though is never going to be an easy task. The current stage of this research has reached a point where I'm combining three models to take a consensus of them all to indicate highly rated home wins, draws or aways. One model looks at the home team's record and ranks the chances of H,D or A, a second performs the same task with the away side's record, and a final model that looks at both records to reach a conclusion. Because of this consensus far fewer selections are generated, the majority of which are for Home wins, Away wins to a lesser extent and I've yet to see them all agreeing on a Draw ~ maybe justifying my view that a tied result is the most difficult results market to beat. However, sticking with my original quest of correct scores, as well as a straight H, D or A bet, each selection is also bet as a correct score using the league's most common scoreline. I'll use the same level stake for each wager, because HDA has a high strike with poor odds (most will be odds-on, but I'm looking for 80% strike) whereas correct scores have better odds but a lower strike rate. My aim is a minimum 10% yield for each bet type. But, the best laid plans . . . Bets for this weekend are; ENG Middlesbro v Swansea 1-0 ENG Nottm Forest v Rotherham 1-0 BRA Flamengo v Ath Mineiro 1-0 CHI Dalian Aerbing v Shanghai Shenhua 2-1 10 pt home win on each. 10 pt correct score on each.
Many football rating methods are compiled on a match-by-match basis in real time, inasmuch a ratings is assessed for a game at hand, then using the result of said game, the rating is adjusted accordingly. The most recent match therefore has perhaps too much of an influence on how the new rating moves. This approach presented here also uses past results to calculate the outcome, but it looks at ALL previous game results and treats them as equally important. Trends therefore are not picked up, whichever way a team's form is heading is totally ignored. A good or bad thing? . . . let's find out. I'm using neural/genetic machine learning to process season-to-date league results to find the best fit of correct home, draw, away predictions to then apply to the next set of fixtures. In order allow the algorithms enough relevant information and data it is thought necessary to employ only where a league season is half completed. Other caveats include no leagues of more than 20 teams so as not to overwhelm the net (and take far longer to process). No leagues with too few teams either where teams may play each other more than twice ~ nothing confuses artificial intelligence more than encountering the same situation with different outcomes, e.g. United v City showing one home win and one away win. Having churned through a few leagues here's what it gives for this weekend; (odds are Oddsportal average odds from 50 odd bookies) CHILE 18-Aug-18 16:00 San Luis U. Espanola A 2.57 3.28 2.59 18-Aug-18 19:00 Antofagasta Union La Calera nobet 1.94 3.54 3.57 18-Aug-18 21:30 U. De Chile O'Higgins A 1.64 4.01 4.51 19-Aug-18 00:00 Everton Huachipato H 2.04 3.27 3.52 19-Aug-18 16:00 Deportes Iquique Colo Colo A 3.19 3.51 2.05 19-Aug-18 19:00 U. De Concepcion Deportes Temuco H 1.83 3.43 3.99 19-Aug-18 21:30 U. Catolica Curico Unido H 1.72 3.57 4.45 SWEDEN 18-Aug-18 15:00 Malmo FF Trelleborgs H 1.15 7.89 17.16 19-Aug-18 14:00 Elfsborg Norrkoping X 2.87 3.44 2.36 19-Aug-18 14:00 Sundsvall Hacken H 3.26 3.51 2.13 19-Aug-18 16:30 Brommapojkarna AIK nobet 7.98 4.47 1.4 19-Aug-18 16:30 Djurgarden Kalmar H 1.65 3.63 5.53 20-Aug-18 18:00 Hammarby Orebro H 1.43 4.64 6.64 20-Aug-18 18:00 Goteborg Ostersunds A 2.93 3.25 2.41 JAPAN 19-Aug-18 05:00 Sapporo FC Tokyo X 2.79 3.27 2.45 19-Aug-18 10:00 Nagoya Sagan Tosu X 2.9 3.4 2.3 19-Aug-18 10:00 Shimizu Urawa H 3.54 3.38 2.03 19-Aug-18 10:00 V-Varen Nagasaki C-Osaka X 3.43 3.4 2.05 19-Aug-18 10:30 Kashima Yokohama M. A 1.92 3.77 3.53 19-Aug-18 11:00 Hiroshima Kawasaki Frontale A 2.88 3.41 2.32 19-Aug-18 11:00 Iwata Kashiwa A 2.74 3.17 2.55 19-Aug-18 11:00 Shonan Kobe X 3.23 3.42 2.13 19-Aug-18 11:00 Vegalta Sendai G-Osaka A 2.09 3.53 3.22 CHINA 18-Aug-18 12:35 Beijing Renhe Beijing Guoan A 7.35 5.74 1.31 18-Aug-18 12:35 Chongqing Lifan Shanghai SIPG A 7.94 5.83 1.29 18-Aug-18 12:35 Dalian Yifang F.C. Henan Jianye H 1.91 3.48 3.77 18-Aug-18 12:35 Guangzhou R&F Changchun Yatai A 2.14 3.62 3.01 18-Aug-18 12:35 Guizhou Zhicheng Shandong Luneng A 6.24 4.93 1.4 18-Aug-18 12:35 Tianjin Teda Jiangsu Suning nobet 2.43 3.38 2.71 19-Aug-18 12:35 Guangzhou Evergrande Tianjin Quanjian H 1.07 10.81 22.57 19-Aug-18 12:35 Hebei Shanghai Shenhua H 2.09 3.72 3.01 I also processed Brazil Serie A, but they're not quite half way through their season yet, but here are the forecasts anyway; BRAZIL OddsPortal average odds 18-Aug-18 20:00 Santos Sport Recife H 1.51 4.01 6.46 18-Aug-18 23:00 Corinthians Gremio A 2.97 2.92 2.58 19-Aug-18 15:00 Atletico-PR Flamengo RJ H 2.74 3.12 2.62 19-Aug-18 15:00 Internacional Parana H 1.38 4.4 8.74 19-Aug-18 20:00 Botafogo RJ Atletico-MG X 2.36 3.31 2.92 19-Aug-18 20:00 Cruzeiro Bahia H 1.44 3.98 8.2 19-Aug-18 20:00 Vitoria Palmeiras X 3.42 3.4 2.08 19-Aug-18 23:00 America MG Fluminense H 2.31 3.09 3.19 19-Aug-18 23:00 Sao Paulo Chapecoense-SC H 1.39 4.31 8.88 21-Aug-18 00:00 Vasco Ceara A 1.75 3.36 4.91
Hello, I have been playing around with a new prediction model for all the Leagues that BBC posts match reports on (all professional English and Scottish Leagues). The number of variables you can derive from the text commentary is quite good, especially for a lower leagues. It is still early phase, and I'm still tweaking some things. The final goal is to have a fully automated workflow that: 1) scrapes every new game from BBC, derives events from text (Shots, Bookings, Goals, Substitutions, Cards, Offside), 2) Aggregates the statistics for each game and applies Expected Goals (based on each individual shot, what is the probability that it should have been a goal) and Expected outcome models (based on the aggregated game statistics, what should have been the result). 4) Computes historical metrics for each team from game statistics 5) Scrapes fixtures and odds from oddsportal (multiple bookmakers) and appends them to a database 6) Applies the prediction model, suggest recommendations and creates visualization. Currently I have only an Outcome model (1X2) but will also attempt including an Over/Under model and BTS model. 7) Posts predictions on a webpage (this is for the future, there is still a lot of work to be done) 8)Keeps track of Bankroll I'm at about 95% of that (excluding 7), but I was hurrying because there are plenty of games today. Purpose: I will monitor the results from the predictions here. From time to time may also be also posting visualizations and explanatory analysis of games. I also have Offensive and Defensive metrics for each team, so maybe they can tell a nice story. Results: From cross-validation results looked decent, (about 10% of games ended with a loss, median yield was about 5.5%) but I'll not get to excited because in the past I had the experience with a model that good results with cross-validation but did awful when using it. Odds, bookmakers: Currently I am scraping best odds from oddsportal, but in practice I will limit myself to: Pinnacle,Marathon,Matchbook Bet selection: My threshold for recommending a bet is Bookmaker odd/My odd - 1 > 0.125. Only these bets are posted. But I can post all of them, but I need a more convenient solution because the images would become to large. Maybe I'll create an album for every league and embed the thumbnails here. First round: Still working on visualizations, they were done in a hurry, quality and size needs to be optimized. Also, please suggest others ways of displaying the data if you have any ideas. Does anyone knows how can I embed a table in the forum posts? I think it is not practical to posts tables as screen captures.