An Investigation of SVM Regression to Predict Longshot Greyhound Races

ABSTRACT In this paper we investigate the role of machine learning within the domain of Greyhound Racing. We test a Support Vector Regression (SVR) algorithm on 1,953 races across 31 different dog tracks and explore the role of a simple betting engine on a wide range of wager types. From this we triangulated our results on three dimensions of evaluation: accuracy, payout and betting efficiency. We found that accuracy and payouts were inversely linked, where our system could correctly predict Wins 45.35% of the time with a betting efficiency of 87.4% (return per bet) for high accuracy low payout, or predict Superfecta Box wagers with 6.45% accuracy and a 2,195.5% return per bet, corresponding to low accuracy high payout. This implied that AZGreyhound was able to correctly identify longshot dogs and we investigate the reasons why as well as the system's performance. INTRODUCTION The ability to predict future events with a certain level of accuracy has its appeal with gamblers and academics alike. This diverse demographic subset seeks to find an edge in predictive sciences, albeit with different motivations. The underlying problem with prediction lies within the problem dynamics, where important parameters are difficult to identify, are constantly shifting and the full effect of selected parameters has not been fully explored. The ultimate question becomes, can profitable predictions be made from the parameters selected? Greyhound racing is recognized as one of the nation's largest spectator sports. According to the American Greyhound Track Operators Association, it is currently legal in 16 states: Alabama, Arizona, Arkansas, Colorado, Connecticut, Florida, Iowa, Kansas, Massachusetts, New Hampshire, Oregon, Rhode Island, South Dakota, Texas, West Virginia and Wisconsin. Other states do not hold live racing but offer simulcasts (broadcasts of remote races) for betting. Greyhound racing has a following that parallels horse racing. Many of the same elements in animal athleticism exist in both sports and also the betting on these races. The key to betting is determining how to systemically predict the winners and combinations of winning bets. Bettors must carefully read all the information on the race card and gather as much information about the dogs as possible. Bettors will examine the dogs and their physical conditions, how they have shown in past races, their breeding and bloodlines, their assigned grades for how well they perform, as well as their odds against the dogs they will race against. Weather also plays a role as some bettors rely on the inside traps (part of the track) during wet races. Even weights of the dogs come into play. Lighter dogs and the longshots tend to get bumped and pushed out of the running in the first couple of turns. Greyhound racing is considered by many to be the most consistent and predictable form of racing (GRAA, 2008). Some expert greyhound bettors recommend keeping bets simple when first starting. Bets on win, place or show are easiest. A win means your dog finished first, place means it finished first or second and show means your dog finished first, second or third. If you can pick the winning dogs in certain categories over a series of races, that will earn more money. Our research motivation is to build upon prior machine learning techniques in the domain of Greyhound Racing and more closely examine the effect of longshots on racing prediction and payouts. We further examine the impact of non-traditional wager types and more robustly examine their resulting accuracy, payouts and return on wagers. LITERATURE REVIEW Predicting race outcomes can generally be broken into three distinct areas: mathematical, psychological and data mining techniques. Mathematical areas include Harville formulas (Harville, 1973) which are a collection of equations that establish a rank order of finish by using combinations of joint probabilities (Sauer, 1998). …