Only data, no code
Bookmakers' bias in sports betting
The authors assume that the readers are familiar with basic terms in sports betting. If not, please use online help to learn, it may take only a few minutes.What is bookmakers' bias?
It is absolutely wrong to assume that bookmakers' models are weak or unprofessional. When any bookmaker offers, let say, an Over/Under bet = +2.5 -110 -110, the even odds for both outcomes are very well estimated. If public starts betting evenly on both outcomes depending on their individual sympathies, than bookmaker's profit will be $10 per each pair of opposite bets. But life is never that simple. Public has preferences, prejudice, bias, superstitions, misunderstanding, , laziness, negligence and so on.When public start making bets which contradict common sense, the bookmaker has no choice but to adjust bets accordingly, make them, let say, +2.5 -130 +110, but the probabilities for both outcomes are still even, and those who identify bias and bet on +110 outcome can make a profit.
Research subject and concept
British soccer premier league. The data is taken from Odds Portal for the years (2008-2009), (2009-2010), (2010-2011). The bets and outcomes for every individual match for these years were copied into machine readable formatted file, like below:# Everton - Chelsea # Wednesday, 10 Feb 2010, 20:00 # 2:1 # 1X2 10Bet +446 +254 -141 5Dimes +470 +271 -133 bet-at-home +425 +275 -167 bet365 +400 +260 -137 bwin +425 +275 -167 Jetbull +400 +240 -143 Pinnacle +437 +280 -133 Unibet +384 +245 -139 # Asian handicap bet365 -0.5 +359 -1000 bet365 -0.25 +309 -769 10Bet 0 +245 -370 bet365 0 +260 -455 bet365 +0.25 +160 -227 10Bet +0.5 +124 -141 5Dimes +0.5 +120 -141 bet365 +0.5 +117 -149 10Bet +0.75 -104 -104 5Dimes +0.75 -109 -106 bet365 +0.75 -105 -102 Pinnacle +0.75 -105 -101 10Bet +1 -152 +131 5Dimes +1 -156 +141 bet365 +1 -161 +133 bet365 +1.25 -227 +169 bet365 +1.5 -294 +204 bet365 +1.75 -526 +285 # Over/Under 5Dimes +1.5 -286 +249 bwin +1.5 -333 +220 Jetbull +1.5 -333 +220 10Bet +2 -175 +148 10Bet +2.25 -118 +102 5Dimes +2.25 -118 +102 10Bet +2.5 +112 -127 5Dimes +2.5 +106 -122 bet365 +2.5 +100 -125 bwin +2.5 -111 -125 Jetbull +2.5 -111 -125 Pinnacle +2.5 +106 -118 10Bet +2.75 +144 -172 5Dimes +2.75 +149 -164 5Dimes +3.5 +272 -312 bwin +3.5 +260 -400 Jetbull +3.5 +240 -357 # endFor example, the bets for 1X2 are followed by 1X2 header down to Asian handicap header, so it all can be elementary read by software utility.
Implementation and result
The first line of 1X2 in file10Bet +446 +254 -141can be converted to expected profit per 100 dollar bet on each outcome in case of success
446, 254, 71Having known difference in goals as 2-1=1, it is possible to train AI as a function approximately supporting equivalence
$$F(446, 254, 71) = 1.0 + error $$
That will make an elementary predictor of bookmaker's expectations corrected to filter biased bets of the public. Using Kolmogorov-Arnold representation it is possible to train AI and predict outcomes. Unfortunately, it does not provide large enough advantage. It gives approximately 45% to 50% successful predictions, which is enough only to beat bookmakers' commission and make a profit near 5% of the size of the bet.The reason for a weak result is that AI models bias for all bets all together, while the public has preferences for concrete matches, teams and arenas.
In order to improve result, the second predictor is introduced. If to consider a matrix with results of a few hundred preceding games
| home\away | team A | ... | team B |
| team A | n/a | ... | 2 |
| ... | ... | ... | ... |
| team B | 1 | ... | n/a |
with differences in goals and apply either of known techniques of matrix factorization, than the unseen outcomes can be predicted. The usual accuracy in this case is also near 45%, however, having another independent predictor, the prediction can be significantly improved, especially in the case when one of them is tuned to predict particular matches and takes into consideration the recent history.
We decided not to publish the concept of combining two predictors (bookmakers and history) for predicting of the outcome and the concept of training another AI to maximize money gain rather than predicting the outcome, but we provide the end result of modelling. Using these two predictors it is possible to obtain statistical profit near 30% of the total money used for betting. For example, for British premier league, in case of betting on all 380 games, 100 dollars per each, the profit in season will be near $11,400.
Can bias be corrected?
No. Bookmakers can make similar or even more accurate models, but the public will bring chaos and enforce adjustment.Conclusion
Using bookmakers' bets for predicting of outcomes is not a new idea. This opportunity was noticed by many other authors, for example:Odachowski K., Grekow J. (2013) Using Bookmaker Odds to Predict the Final Result of Football Matches. In: Graña M., Toro C., Howlett R.J., Jain L.C. (eds) Knowledge Engineering, Machine Learning and Lattice Computing with Applications. KES 2012. Lecture Notes in Computer Science, vol 7828. Springer, Berlin, Heidelberg.
Online copy is available.
The approach in published paper is different. Authors tracked changes in bets for the short period before the match and trained AI to predict outcomes based on changes in bets and not by final values. However, it showed the same tendency of bias in bookmakers' bets forced by balancing expected commissions by making certain bets more and others less attractive.
