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Sports betting analysis

Just listen to what Arsenal former manager, Wenger had to say about him:. These debates that I hear are a joke, a farce. People [Merson] who have managed zero games, they teach everybody how you should behave. No matter what your opinion about him, the prediction of an ex-Arsenal player for the Arsenal-Man United match will surely be more dependable than an obscure model that runs on randomly spitting out numbers.

Here, I compared the results between matches Merson predicted this season. He achieved a The result startled me. And I did not even have to do much besides asking the beloved Poisson processes to chunk out numbers. This is when I started looking into sports betting. If you ever think that the terms and quoted APR on your credit cards are complicated, try venturing into those betting websites once.

They are just plain crazy. Take the US Odds for example. This is fine, but then they have negative odds , like an odds. I mean, they are still using Feet and Fahrenheit anyway. For the purpose of this project, we will use a nicer system: the European Odds. For example, Bet gives an odds of 2. But things are not always nice and simple. In reality, to maximize profit, bookmakers employ teams of data scientists to analyze decades of sports data and develop highly accurate models for predicting the outcome of sports events and giving odds to their advantage.

That extra 2. To get the real probabilities, we need to correct for the profit by dividing through by For a perfectly efficient bookmaker, these are the probabilities of each outcome. The expected profit is the same if I had betted for Man United:. And — you guessed it — if I bet on a draw, I expect to get back 97 cents.

This understanding does not stop me from trying to exploit any potential inefficiencies in the market. At first, I devise the general bet strategies. Implementing the Kelly Criterion is quite simple in R:. However, if we aggregate all the odds from many different betting houses, we should get a better reflection of how bookmakers view the probability of an event, Arsenal defeating Man United for example:. Obviously, there are inherent risks in this optimal Poisson model. Both Merson and the Poisson-process model and me!!!

All in the same weekend!!! Before you clone my Github repo and raise capital for your sports hedge fund, I should make it clear that there are no guarantees. If anything, this article is a toy example of what you could potentially do. But the bookmakers have made it extremely difficult for anyone to gain sustainable profits. If there are still a lot of people placing a bet at 4.

Chances are that by the time the code infers the most optimal odds, it has been changed. Furthermore, if you do start to make a regular profit, bookmakers can simply thank you for your business, pay out your winnings and cancel your account. This is what has happened to a research group from the University of Tokyo [3].

A few months after we began to place bets with actual money bookmakers started to severely limit our accounts. If you enjoy this article, you may also enjoy my other article about interesting statistical facts and rules of thumbs. For other deep dive analyses:. The entire code for this project can be found on my Github profile. Bell System Technical Journal. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Australians are among the most prolific gamblers in the world, with estimates forecasting total gambling expenditure will increase 3.

These figures are consistent with reports from major wagering operators indicating that Internet wagering revenue is increasing more rapidly than retail and telephone betting. In the third quarter of the financial year, wagering provider Tabcorp reported an increase of Internet gambling is prohibited in Australia, with the exception of online wagering and lottery provided by licensed operators.

The most common advantages cited by players to explain the use of Internet gambling included the lack of travel needed, convenience and accessibility, the lack of crowds and unpleasant people, greater privacy and anonymity and greater physical comfort. These results indicate that although they could bet in venues or via the telephone, an increasing proportion of bettors prefer to use the Internet. Previous research has had to rely on inaccurate self-reports of betting behaviour but the use of data from player account-based gambling has begun to make a substantial contribution to the understanding of gambling behaviour and player profiles Gainsbury Analysis of the betting patterns of 40, Internet sports gamblers that subscribed to a European wagering site in provides an insight into the typical behavioural patterns of these relatively early adopters of online gambling LaBrie et al.

Over an 8-month period the vast majority of fixed odds sports bettors appeared to bet in a relatively moderate fashion, placing an average of 2. A small proportion of bettors appear to be highly involved in terms of the amounts wagered or number of bets placed. An alternate explanation is that a small proportion of online bettors are more sophisticated and use resources and strategies to reduce their losses, who may also receive favourable odds and rebates from operators due to the size of their wagers.

The players studied were most likely to be young German men and given that the wagering analysed was done in the extent to which the results can be applied to other populations today is unclear. Furthermore, although fixed-odds and live-action bettors were compared, no analysis was conducted based on the types of bets made or events bet on, which may provide useful insights about player behaviour.

A more recent analysis of player account data analysed the wagering activity of 11, customers of a large Australian totalisator wagering operator over a year period, from to Gainsbury et al. On average, players were active for a period of 5. Although these results only represent bets placed with one operator, they provide valuable insight into typical betting patterns.

However, without a detailed analysis of all player behaviour it is difficult to put these figures in context. The current study aimed to analyse player account data from an Australian corporate bookmaker. Investigation of online wagering patterns will further the understanding of how individuals wager online, including the types of bets placed and the outcomes of these. This information can be used by stakeholders, including policy makers, who may be able to implement a more targeted approach relevant to this type of gambling.

Given the few studies conducted examining online wagering, and that this is the first study to examine player account data from a corporate bookmaker, no specific hypotheses were formed. This study aimed to provide a descriptive outline of the types of bets made and the outcomes of bets and compare wins and losses and gambling outlay and returns.

These variables were chosen as the focus of this study as they were available in the dataset and have been examined in previous populations of online bettors so will contribute and extend the extant literature on this topic. An Australian corporate bookmaker, Betchoice now trading under the name Unibet provided a database of de-identified account information for research purposes.

Betchoice was formed in and licensed in the Northern Territory to provide online wagering in addition to telephone betting in The principle focus of Betchoice was providing bets on races as opposed to sports, although a wide variety of markets are available for customers. Bookmakers offer fixed-odds bets, the odds may change during the betting period, but the odds for each bet is determined at the time the bet is placed.

The dataset contains bets and transactions placed online through the Betchoice website and via telephone from January 1, until December 31, In total there were 3,, lines of information, including , payouts, , money transfers into and out of accounts, , bets between bookmakers, 21, cancelled, 6, bets unfinalised, rejected bets and 15 accepted bets that had not been completed.

When these were removed, there were 2,, completed bets from 12, different user accounts. The vast majority The dataset included a unique user identification number for each bet. No demographic data were available, so all analyses were conducted based on bets placed.

Two variables were included in the dataset that recorded the size of the bet. One recorded the amount of money placed by the bettor bet outlay , while the second recorded the overall size of the bet, including any bonuses. Where an analysis is reported below for bet outlay, the same analysis was tested for total bet size. The pattern of results was the same for bet outlay and total bet size for all analyses.

For gambling return means, values with the same superscript do not differ significant from each other. Total bet size including free bets and adjustments was notably different for exotic and each way only bets only. All bets were recoded as either a win or a loss, based on the bet status variable.

Wagering return was recorded by a separate variable, where a loss is recorded as negative total bet size, while a win is recorded as whatever the bettor won above their total bet size. In some cases, despite the bet being recorded as a win, the gambling return was negative. The winning amount for multi bets was not present in the dataset and was estimated by multiplying the total bet outlay by the recorded dividend and subtracting the total bet outlay from the product. To avoid undue influence of extreme values, bet outlay, total bet and gambling return variables were winsorised so that values beyond the 1st and 99th percentile were recoded as having the same value as that percentile.

However, when minimums and maximums are reported, this refers to unwinsorised data. Due to the extremely large number of bets being analysed, almost all inferential statistical results were significant at an alpha of 0. Multi-bets were treated as a single bet and distinct from the other categories, even though there may have been some cross-over, for example with a multi-bet that consisted of legs that were all placed for a win. The vast majority of bets There is no distinction in the data file between different types of horse racing harness, trots, etc.

The second highest category was sports betting Thus, the vast majority When all forms of football were combined soccer, Rugby Union, Rugby League, Australian Rules, American football , they accounted for 6. Football bets were largely accounted for my soccer, rugby league and Australian Rules and bets on non-football ball sports were most likely to be on basketball or tennis. In addition to the single sports bets, 1. Only bets 0. Total bet size was notably different for horse racing only.

The types of bets with the highest losing percent of bets were multis Slightly more than half of both handicap and total bets were wins. Mixed bets were the least successful This latter category consists mostly of election and social bets. Of the 2,, bets placed, 0. In terms of winsorised bet outlay, all bet types were significantly different from each other except for draw, multis and place, Brown-Forsythe F 8, The greatest bets were the more specialised handicap and total bets, and although these also had high levels of variation, the medians were still significantly higher than other bet types.

Bettors also wagered greater amounts on win and margin bets. Statistically significant differences were also found between the various bet types in terms of gambling return, Brown-Forsythe F 8, The greatest returns were for handicap and total bets, followed by place bets. When comparing the six categories racing, football, ball sports other than football, non-ball sports, mixed and other in terms of mean winsorised bet outlay, all were significantly different from each other except for racing and mixed bets, Brown-Forsythe F 5, The greatest bets were placed on novelty and non-football ball sports, followed by football, non-ball sports, and then mixed bets and racing.

Median bets are in a slightly different order, demonstrating the variance in results. Within non-football ball sports, the greatest best were placed on basketball, other ball sports, and tennis and within football, the greatest bets were placed on American Football, rugby union and Australian Rules.

In terms of gambling return, novelty bets were significantly higher than all other bet types, whereas racing was significantly lower than non-ball sports and ball sports other than football. Mixed bets and football bets were not significantly different to each other or racing, non-ball sports and ball sports other than football, Brown-Forsythe F 5, Within non-football ball sports, other ball sports, tennis and basketball had the greatest returns and within football, rugby union bets had the greatest return.

Consistent with the aim of this paper, the betting patterns of gamblers using an online bookmaker have been described, based on a database of all bets placed in one calendar year. Approximately half of all bets were placed for a win, which is similar to the results reported by another Australian betting operator, Tabcorp Masters This may indicate that bettors prefer simple, uncomplicated bets, and are more comfortable identifying just one potential winner.

This may also be considered a bolder betting style, as opposed to bets each way, which reflect a more conservative, cautious, and risk-averse approach. Place bets were more successful than win bets, both in terms of percentage of bets that win and in gambling return, although the latter could simply be due to larger bets being placed for wins.

That a higher proportion of place bets were successful compared to wins may not be very surprising, given than there are more possible successful outcomes for place bets than for win bets. Exotic bets and multi bets are relatively risky, as they require several predictions to be fulfilled, although, as a result they generally give a larger return if successful. As risk preferences and motivations for placing types of bets were not measured these hypotheses should be explored in subsequent research.

Given that the operator is predominantly a race betting specialist, it was not surprising that the vast majority of bets were on races. This is consistent with the Australian wagering market, which is dominated by race wagering, despite the rising popularity of sports betting.

Sports betting was dominated by ball sports and various codes of football, in particular soccer, rugby league and Australian Rules. However, this has largely been driven by the popularity of in-play betting, which is not permitted on regulated Australian wagering sites. The findings also likely reflect the greater consumer interest in and popularity of various sporting events. Slightly different results are found for watching sports on television, which is led by cricket, Australian Rules football, tennis, rugby league and the Melbourne Cup Roy Morgan The betting patterns are consistent with US research which suggests that betting trends follow popular sports spectatorship and bettors, like fans, appear to enjoy seeing the best teams and preferring games televised on major networks Paul and Weinback , The bookmaker analysed in the current study was not heavily advertising before or during the period analysed, so these results are unlikely to be influenced by marketing tied to sporting events.

It is possible that these findings support the notion that betting and watching sporting events are complimentary activities for most bettors, as opposed to betting being an independent activity related to maximising profitable returns Paul and Weinback However, as motivations for betting were not directly measured in the current research these hypothese require investigation in future studies.

Overall, the majority of bets resulted in losses, which expected based on previous studies of wagering Gainsbury et al. The results of this study may imply that the bookmaker studied is appropriately skilled in predicting the outcomes of events and exploiting this advantage by strategically setting odds that ensure that the majority of bets are lost, or at least return an affordable proportion of wagers to customers so that they continue betting over time, but are unlikely to make a profit.

This is consistent with previous research showing that the market for sports gambling is structured to allow bookmakers to achieve substantially high profits Levitt Furthermore, many large bets were placed, including risky multi bets, although the majority of bets were for reasonable amounts, depending on how many bets each customer made over a period of time. This may reflect a tendency for customers to place larger bets with an online bookmaker than a totaliser agency.

Given the complexity of this topic, an analysis of individual bettor behaviour is the subject of another paper. The most popular type of bet, to win, had a relatively high rate of losses and the lowest average gambling returns, which may be explained by the predominance of racing bets and the greater difficulty in picking one winner in a field with multiple contenders.

Alternatively, these bets may be more likely to be placed by less sophisticated customers who have a lower ability to accurately assess the relevant factors and pick the best event and winner to successfully back. For example, bettors have been shown to have a bias to bet on longshots in pari-mutuel horse-race betting Golec and Tamarkin ; Jullien and Salanie ; Vaughn Williams and Paton , which may also be true for fixed-odds betting.

Similarly, bettors have been shown to bet a disproportionate amount on home teams and underdogs Golec and Tamarkin ; Gray and Gray ; Paul and Weinbach , If odds are balanced, bettors should place bets evenly on each side or outcome, therefore, the outcomes of previous research may imply that bettors may be influenced by emotions and preferences when placing bets, rather than probability of economic outcomes.

Furthermore, Levitt found that sportsbooks can consciously exploit the desire of bettors to back the favourite team by giving bettors on that team poorer odds. The cognitive processes and strategies used by customers in selecting bets should be examined in future research. Multi-bets represented a significant portion of bets, but customers tended to bet lower amounts than for wins and places, perhaps suggesting that they may be less confident in the outcome.

This strategy may be appropriate given that multi-bets resulted in the highest proportion of losses. The lowest average bets were for exotic bets, which also had a relatively low standard deviation, indicating that most customers wagered small amounts on these bets, which are based on very specific outcomes. Again, unsurprisingly, the vast majority of exotic bets resulted in losses, suggesting that low bets are appropriate, although the returns for these bets were more favourable than for wins, as these bets typically have good payout rates if successful.

Lower amounts were generally placed on each way bets, which may be due to the lower odds typically offered for these bets, so players do not have the same opportunity to win large payouts. Correspondingly, each way bets had a lower proportion of losses, likely related to the greater number of outcomes to result in a winning bet. Each way bets may be more popular with risk-averse bettors, who would be happy with small outcomes, but reduced losses.

This perceived insurance may lead some bettors to place an each way bet instead of taking the risk of either a win smaller chance of a higher return or place higher chance of a smaller return and thus engaging in a bet when they would not do so if the each way option were not available.

Of interest, the more specific handicap and total bets were placed by few customers, but these represented the largest average bets and the greatest return to players, although there was also large variation in these results. On average, half of the place and handicap bets made resulted in wins, in contrast to all other types of wagers.

These results are quite significant as they suggest that it is possible for bettors to come out ahead. Customers who made these types of bets may have greater knowledge and understanding of specific races and the factors that affect outcomes and subsequently be more successful with their bets. Alternatively, they may be more astute in finding bets with favourable odds. These results are consistent with the findings from an analysis of another Australian wagering operator, which found that a small proportion of bettors were able to minimise their losses Gainsbury et al.

This study did not investigate the type of bets made by the more successful bettors, nor did the work by La Brie and colleagues There is evidence of subgroups in the heterogeneous population of bettors, including demonstrated differences in betting behaviour and performance between recreational and professional bettors Bruce et al. Bets placed on races tended to be smaller on average compared to other events. However, those betting on races tended to lose more money per bet, on average, than all other forms of betting apart from mixed bets.

This may reflect the interest in race betting as an entertainment activity rather than an expectation of consistent winning or positive returns. In relation to sports betting, bet outlays and outcomes differed between the types of event bet on, indicating that customers had different betting strategies for different events. Mixed bets placed on multiple sporting events were the least successful, which is not surprising as a greater number of successful predictions are required to win.

This may explain the relatively few bets placed on these events and lower bet outlay compared to other sports. Football bets were on average significantly smaller and less successful than bets placed on non-football ball sports. Soccer, Rugby League, and Australian Rules games appear to be particularly difficult to predict, judging from the lower returns. In contrast, bets on Rugby Union, had a positive return on average, although over half still resulted in losses, perhaps suggesting that some astute customers were able to identify more favourable odds for rugby union matches and subsequently won large amounts, impacting the overall average results.

An alternative explanation may be that events in which outcomes are more difficult to pick have less favourable odds, given no clear likely winner, resulting in a lower proportion of returns. Given the absence of prior studies examining types of bets made on sports events and the outcomes of these, including the motivations of bettors, interpretation of these results requires some caution until further research can be conducted.

Similar to the outcome of more specialised racing bet types, bets placed on less popular events, i. Wagers placed on basketball, baseball, and ice hockey had a lower proportion of losses than football bets. Similarly, tennis, basketball, cricket and ice hockey bets had the greatest average returns not including rugby union and other ball sports , although these were still losses on average. However, there was also relatively high variability in results indicating that the outcomes of bets on these events are not consistent.

Because betting on these events is less popular, these events may be subject to less scrutiny and research by the bookmaker. Novelty events include bets placed on the outcome of elections and entertainment or popular events, such as who will win the latest reality television show. Few bets were placed on these events, which is unsurprising as these are generally intended to be non-serious bets that are provided for interest and novelty value.

However, novelty bets had the highest returns. This may reflect a tendency for the operator to be willing to lose some money on these types of bets, which are primarily designed to generate interest and discussion, as opposed to contributing to profits. As the dataset included all bets placed over an entire year, the results are relatively robust in terms of capturing seasonal variation in betting patterns.

However, it is important to note that betting patterns are related to major sporting events and was slightly unusual in having two AFL grand finals, as well as a Soccer World Cup. The dataset only accounts for betting with one operator, which specialises in race wagering, so the results may not be representative of online wagering on sports with other operators more generally.

Bets placed with other operators are obviously not accounted for, nor is other gambling involvement, limiting the description of overall betting patterns. Furthermore, despite the richness of information obtained from actual behavioural data, no contextual data or personal variables were included so it is not possible to understand the customers beyond their behaviour. Future studies should attempt to combine self-reported data, for example, other gambling behaviour, personal characteristics, thoughts and attitudes, and risk for problem gambling to provide a more holistic account of online bettors Gainsbury Notwithstanding the limitations, the strength of this analysis is the longitudinal analysis of an entire sample of active bettors, which accurately describes all bets made during the period examined.

This represents a significant contribution to the field as it is the first paper to specifically examine the types of bets placed on events and outcomes. It enables for the first time, a detailed understanding of how players place bets and the typical outcomes. The results of these analyses show that overall, although the majority of bets place result in losses, these losses were relatively moderate.

Wagering operators tend to return a larger proportion of bets to customers, unlike casino games and slots, which tend to offer less favourable player returns Church-Sanders Further research is required to specifically compare betting patterns and outcomes for wagering and gaming, which is important to determine whether different policies and regulations should be applied to these activities.

After race wagering, which was the predominate activity of the online wagering operator examined, bettors appear to be most likely to place bets on popular sporting events, suggesting that this activity is an adjunct to watching sports and races. As the majority of bets resulted in losses, this suggests that sports and race wagering may not be an activity in which the customers aim to make money, as otherwise they would cease betting in the face of continual losses.

Rather, it supports the notion that, for at least some customers, wagering is an entertainment activity, motivated by factors other than simply winning money. Further research is required to examine the individual factors and motivations for betting. Ideally, any research in which contextual details about individual bettors could be linked to player-account data as this would provide an extremely useful insight into how motivation affects actual gambling behaviour. Further research may also identify specific betting patterns and subgroups of players.

This may lead to the development of different consumer protection and responsible gambling strategies for players based on their individual gambling patterns Gainsbury Optimally, combining self-report with analysis of actual player behaviour may provide practical insights into betting behaviour for key stakeholders, including policy makers, and lead to the development of appropriate regulation that does not overly restrict recreational players, but offers appropriate safeguards.

The authors would like to acknowledge and thank the European Association for the Study of Gambling who provided funding for this research for the first author as well express their appreciation to Unibet and Mr. Mark Morrissey who provided the data analysed in this paper. Sally M. Alex Russell, Email: ua. Read article at publisher's site DOI : J Behav Addict , 8 2 , 07 Jun J Gambl Stud , 34 3 , 01 Sep J Behav Addict , 6 4 , 13 Oct

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