Crowd Wisdom In NFL Point Spread and

Over/Under Betting

Group 4: Jonathan Bell, Tyler Ventura, Sam Cantor, Alan Gadjev, Nate Mays

Background

Sports Betting is a growing and already substantial business in the United States. Until recently, related betting was only legal in specific areas of the United States such as Las

Vegas, Delaware, Montana, and Oregon.1 Under the Professional and Amateur Sports Protection

Act of 1992 (PASPA), was banned for all sports excluding parimutuel horse and dog racing and jai alai.2 While sports betting is limited in availability, as of November 2017 $4.9 billion per month was legally bet on sports in Nevada alone. Despite the prohibition, illegal sports betting is very popular in the United States. As of the Supreme Court case Murphy v. ​ National Collegiate Athletic Association (2018), the PASPA was found unconstitutional and the ​ decision to sponsor sports related was relegated to the state governments.3 Since this ruling, a number of states have brought forward bills related to legalization of sports betting.

Ohio State economist Jay L. Zagorsky conservatively estimates that the total value of the sports betting will be $70 billion dollars, but many argue that the value will be approximately

$150 billion, with largest estimates at close to $380 billion.4

This project will focus on sports betting on the NFL, specifically point spread betting and over/under betting. Point Spread betting is a form of sports gambling that is particularly popular in Las Vegas . Spread betting involves the setting of a “favorite” and an “underdog” by oddsmakers, and the assigning of additional points to be used when calculating the score. A bettor picks the favorite (F), minus the number of points (B) that they have been docked

(F-B=A), or picks the underdog (U), plus the number of points (B) that they have been spotted,

1 https://en.wikipedia.org/wiki/Sports_betting#1970s-2018:_Prohibition_on_sports_betting ​ 2 https://www.govinfo.gov/content/pkg/STATUTE-106/pdf/STATUTE-106-Pg4227.pdf ​ 3 https://caselaw.findlaw.com/us-supreme-court/16-476.html ​ 4https://theconversation.com/market-for-illegal-sports-betting-in-us-is-not-really-a-150-billion-business-966 18

1 (U+B=C), and the winner is determined by if the modified score totals A>C or A

For this project, crowd wisdom will be represented by Wunderdog Sports compilation of

“Public Consensus.” These projections, scraped from other public betting sources, offer the percentage of the public that selected the “favorite” or “underdog” and the “over” or “under” for

NFL matchups in the 2017-2018 and 2018-2019 seasons. The “expert” in this project will be represented by the Wunderdog Sports Computer algorithm predictions. Wunderdog, a subscription-based service, does not publicly offer their final bet predictions, but does offer a free record of their computer algorithm projections. Their actual algorithm is not publicly available, but considers “statistics, power ratings, and hundreds of very high-percentage proprietary historical situational systems. The systems purposefully avoid hunches, ‘soft’ data or personal gut opinions. I look for agreement between all of my sources which results in a few games selected, but they are the cream of the crop.”5 The public therefore benefits from consideration of injuries, trends, situations that are not factored in to Wunderdog’s Computer

Models (these are factored in to Wunderdog’s Predictions after the computer calculations and are not publicly available).

5 https://www.wunderdog.com ​

2 Literature Review & Similar Studies

Prediction markets attracted the attention of the public after James Surowiecki published the The Wisdom of Crowds in 2004. It sparked the popularity of prediction markets. Since 2004, ​ ​ a number of studies have been published that examined how to harness crowd wisdom. We have looked at other studies that explore the ways crowds can enhance our predictive abilities for sporting events.

The first study we analyzed is titled “Are Crowds Wise When Predicting Against Point

Spreads? It Depends on How You Ask” and was published by a group of research professors ​ ​ from various universities. The study tests Surowiecki’s hypothesis that “the judgements of a crowd (as measured by any form of central tendency) will be relatively accurate, even when most of the individuals in the crowd are ignorant and error-prone.”6 Points spread in the NFL and other sports are argued to be an accurate representation of the crowds’ opinion, however, there has also been evidence that crowds are bad at betting against the point spreads and bet too much on the favorite.7 This study tests Surowiecki’s hypothesis by analyzing the way the crowd performed in betting against point spreads that were adjusted to favor the underdog ( i.e. points spread was increased). The study was conducted over 17 weeks of the NFL season in which more than $20,000 (money was put up by conductors of the study) was wagered. The betting was open to anyone in the United States, so the overall population of the participants was geographically diverse. The aggregation mechanism allowed the conductors of the study to

6 http://www.asecib.ase.ro/mps/TheWisdomOfCrowds-JamesSurowiecki.pdf ​ 7

3 perform statistical analyses. As we can see above, all conditions of Surowiecki’s “wise crowd” were satisfied in this study.

The study investigated three hypotheses, the first of which was that “crowds will wisely choose against biased point spreads even when they are not told that the spreads are biased.”8

Hypothesis 1 was rejected and it was found that the crowd unwisely bet on the favorite in 89.4% of the cases (p < .001), the crowd lost 56.8% of the wagers made and overall performed worse than 93% of participants.

Hypothesis 2 predicts that “crowds will wisely choose against biased point spreads when they are told that the spreads have been increased”. The participants who were told that the point spread was tampered with and to ensure they were informed they had to read the following statement before making any predictions:

“Although official point spreads are designed to give each team an equal chance to win the bet, the point spreads inserted below are not necessarily the official point spreads. In fact, some of the point spreads have been increased, though none of them have been decreased. If you have read these instructions, please click the box below.”9

Hypothesis 2 was rejected and it was found that the crowd unwisely bet on the favorite in 82.7% of the cases (p < .001), the crowd lost 57.9% of the wagers made and overall performed worse than 97.4% of participants.

The third hypothesis tested whether the crowd had the ability to learn and correct itself along the study: “even if crowds are unwise at the start of the study, they should improve over

8http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C00B25FFFC2AD49E56B03502ED761A67?do i=10.1.1.153.8517&rep=rep1&type=pdf 9 Ibid.

4 time, as the crowd’s members accumulate evidence of the inferiority of favorites.”10 Hypothesis

3 was rejected and there was no statistical evidence to support that the crowd’s performance got better or worse over time. Throughout the entire study participants bet heavily on the favorite and failed to learn from their unsuccessful predictions.11

Lastly, instead of betting against the spread and simply trying to predict a team that beats the spread, participants were simply asked to predict a winner and a point differential between the two teams. Participants in this bucket performed much better than what we have seen above: overall the crowd predicted the underdog in 82.7% (p < .001), correctly predicted the team that beats the spread 55.4% of the time and performed better than 95.6% of the crowd.12

The study above showed us that the crowd is not wise when dealing with the conventional ways the betting world operates in (betting against a points spread). However, a prediction market that would ask the participants to predict a point differential would act as a successful prediction mechanism the majority of the time.

The second study that we chose to take a closer look at is titled “Testing the Effectiveness of Semi-Predictive Markets: Are Fight Fans Smarter Than Expert Bookies,” conducted by Sean

Wise, Milan Miric and Dr. Dave Valliere from Ryerson University. To test for “wisdom of crowds” this study looks at UFC (Ultimate Fighting Championship) main-event cards for a three way period. One important aspect of this study is that UFC main-events are one-off events and differ from football in the sense that fighters rarely fight each other more than once, thus

10 Ibid. 11 Ibid. 12 Ibid.

5 mitigating any of the biases. Even if they do it usually takes at least 6 months to schedule a rematch. The authors of the study state that “one-off events serve as a better proxy for assessing the predictive capacity of a crowd vs. experts as they remove many of the biases which arise when new information emerges”

The study compares the predictive abilities of crowds vs. expert bookies in the binary outcome, one-off scenario of UFC events, thus eliminating potential biases towards an irrational outcome. The betting for each of the UFC “main-event” fight from March 3, 2007 to

August 8, 2009 collected from various resources such as official UFC blogs and

“.com” which is an odds-making website. They were then simplified into a binary

“win” for the favorites and “lose” for the underdogs.13 Crowd predictions were obtained from the over-the-text fan voting that is live up until the preliminary fights of the night start. Fans would vote for one of the two fighters and UFC publishes the results of the fan votes before they start of every fight. The percentages of fan votes were then converted into a binary outcome similarly to the way bookie’s odds were. Whichever fighter received the majority of the votes was the fighter that the crowd predicted to win. During the duration of the study, ’s odds were available for 33 fights while fan votes were only available for 21/34 fights.14

The results of the study demonstrated that “fans were able to accurately predict the winner in 85.7% of the cases, while bookies were able to accurately predict the winner in only

13https://www.researchgate.net/profile/Dave_Valliere/publication/248607696_Testing_the_effectiveness_o f_Semi_-_Predictive_Markets_Are_fight_fans_smarter_than_expert_bookies/links/57502c6d08aeb753e7 b4a3d5/Testing-the-effectiveness-of-Semi-Predictive-Markets-Are-fight-fans-smarter-than-expert-bookies. pdf 14 Ibid.

6 69.7% of the cases.”. To obtain further statistical validity of their results, authors of the study conducted a two-tailed t-test of difference in means. The t-test showed a significance of p = 0.19.

The study was unable to reject the null hypothesis and conclude with confidence that crowds are better at predicting fight outcome than bookies. As the authors suggest, the reason for such low level of significance is the small sample size.15

Purpose

This project will seek to analyze a comparison between the public consensus and the computer algorithm with an eye towards a number of potential implications. These potential implications include analysis of influence on the the aspects not included in the computer algorithm on outcome, including injuries, trends, or other situations. Additionally, the results should show implications of “favorite” bias towards most popular teams and “favorite-longshot bias” towards popular underdogs, and how these two biases could potentially be considered when formulating betting strategies. Most notably, the value of following Wunderdog Sports computer predictions or betting in-line with the popular consensus can be compared with this data to the potential results of other notable betting strategies, including random selection or always picking the underdog or favorite. We hypothesize that, based on Surowiecki’s arguments in The Wisdom of Crowds, there will be a statistically significant advantage in using the “public ​ ​ consensus” to predict the point spread and over/under outcomes of NFL games in comparison to using Wunderdog’s Computer Prediction model or other popular betting strategies such as

“fading the public.”

15 Ibid.

7 Data - Sample Selection

This data was all collected through the site, Wunderdog, a subscription-based handicapping sports betting service. The site offers free information on past sports games such as the NFL, college football, the NBA, and college basketball. Among this information they provide free computer predictions of the straight up winner, against the spread winner, and what the over-under will be. Additionally, one of its most useful data points for us was their inclusion of public Las Vegas bets placed and aggregated on their site. It includes the number of bets per team for the against the spread, the number of bets placed on the over or under, or the number of bets placed on the money line (which we did not use for this study). Lastly, it provides the final results of the game to show which bets won. We collected NFL point spread and over/under data from the 2018-19 season, the 2017-2018 season, and the first 8 weeks of 2016-17. These were all regular season games, with the exception of an addition of the 2019 and 2018 Super Bowls.

Given that there are a total of 256 games played per season (32 teams, 16 games each), this meant we used data from a total of 642 games (2.5 seasons + 2 Super Bowls). The data collected from Wunderdog included: the computer prediction for the against the spread, the computer prediction for the over-under, the public prediction for against the spread, the public prediction for the over/under, and the percent of the public that bet on the public consensus against the spread choice, the percent of the public that bet on the public consensus over-under, and the final result of the the point spread and over/under.

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Figure 1. A sample screenshot of our data. Week refers to the week of NFL season the data was collected, ATSResult is the ​ result against the spread, 1 if the favorite covered and 0 if the underdog did. ATScomp is what the model picked, again 1 for favorite and 0 for underdog. PublicATS is who the public chose against the spread and pctATS is the percentage of the public that bet on their choice. The same format for variable names follows for Total, which is over/under betting. 1 refers to the over and 0 refers to the under.

Although this data seems reliable, we do not know exactly where this data is coming from. It’s safe to assume that it comes somewhere from Vegas although we do not know exactly which betting vendor or aggregate they are getting the public bets from. Additionally, the computer model is not publicly available, but explained as: “statistics, power ratings, and hundreds of very high-percentage proprietary historical situational systems. The systems purposefully avoid hunches, "soft" data or personal gut opinions.” Importantly, each of the percentages listed are based solely on the number of bets made by people in the public and do not factor in the monetary value of the bets. This means that 10 people making $1 bets would be the same in our model as 1 person making a $10 bet. Although using dollar figures for this would have been better, we were unable to procure that data and had to rely on the number of large and small bets being roughly evenly distributed.

9 The histogram in Figure 3 below shows a somewhat typical distribution of the public’s consensus levels on a given against the spread bet. Most public against the spread bets were relatively 50-50, as you can see in the left-most part of Figure 3, where the public was split on who would cover the spread. As you can expect, the overall trend of the histogram sloped downwards, implying that the public less frequently felt certain on the outcome of the sports bet.

There were still a surprisingly high number of cases observed where the public consensus was

70% or greater.

Figure 3. Histogram showing the distribution of the public Figure 4. Histogram showing the distribution of the percent ​ ​ against the spread consensus based (note: does not factor in public consensus on over-under picks (again does not factor dollar figure of each bet, only how many people picked that in dollar figure of each bet, only how many people picked bet). that bet).

Figure 4, as shown above, is a histogram with the distribution of public consensus for over-under bets. This histogram had more abnormal results where there the general peak of the

10 distribution occurred around 60%, which implies that the public consensus always seemed to favor the over and did so at a high rate. The public seemed confident in general that the over would hit and there were not many situations where the public consensus was 50-50. This makes it seem like were not doing a great job at picking the line for the over/under, but the over/under seems appropriate as the public was consistently wrong in their over/under bet.

Data - Analysis

The collected data was used to answer key questions central to crowd wisdom in relation to sports betting. Namely, if the expert (in this case, the wunderdog computer model) or the crowd (the public consensus), would outperform the other and if there would be a significant difference between the two. To test this we used logistic regression, a predictive analysis. ​ Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables16. Since we were separately interested in both the spread results and the totals, there were two separate regressions being run. In the first regression, the dependent variable was the result of the outcome of the spread, or ATSresult. We ran the regression with the independent variables ATScomp, PublicATS and PctATS which are the computer models selection, the public consensus selection and the percent of the public that sided with the consensus respectively. For the second regression, the same concepts apply but instead of against the spread the variables used were all relating to the total. Results of these regressions can be found below in figures five and six. ​

16 https://www.statisticssolutions.com/what-is-logistic-regression/ ​

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Figure 5.Logistic regression with the against the spread Figure 6.Logistic regression with the against the total ​ ​ variables variables

Our null hypothesis (H0) for the first regression is that there would be no significant ​ ​ difference between randomly selecting the favorite or the underdog or using the public consensus/computer model to make your decision. The same hypothesis was being tested for the second regression. In looking at the outcomes of both of these regressions, we observe different results. In the against the spread regression, none of the variables were significant at the 10% level. Using this information, we fail to reject the null hypothesis. In the second regression, while the public’s picks and the percent of the public consensus were both still insignificant, the variable representing the computer model was significant at the 1% level. This means there was less than a 1% chance that the results from the computer model were due to chance, meaning that we can reject the null hypothesis that there would be no significant difference between randomly selecting the over or the under and following the computers picks.

Following the regressions, the next step was to run T-tests looking at differences in ratios between the public consensus and the computer mode’s accuracy in forecasting the spread and

12 the total. While in the first regression, neither the computer or the public were significant variables we decided to run a t-test to see if either significantly outperformed the other.

Figure 7.T-test for statistical significance performed on Computer Model and the Public’s against the spread selections ​

We observe that while the public consensus outperformed the computer model, picking correctly 183 times compared to only 164 correct choices from the computer model, it was not statistically significant at the 10% level with a p-value of 0.1693. Combining this result with the previous knowledge that we could not reject the null hypothesis, it seems that there is no discernible betting advantage to be gained from either following the computer model or the public consensus in relation to betting against the spread.

The results changed however, when we looked at the T-test relating to betting the point totals however, seen below in figure 8.

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Figure 8.T-test for statistical significance performed on Computer Model and the Public’s point total selections ​

In this case, we observe that the computer model significantly outperformed the public consensus, picking correctly 209/343 times (good enough for 60.9%) compared to the public only hitting 159/343. The corresponding p-value of 0.0001757 is statistically significant at the

1% level, meaning there is less than a 1% chance that these observed results were due to chance.

In this case, Wunderdog’s computer model vastly outperformed the public and if followed for this period of time would have likely led to a significant profit. If you were betting $10 a game for a profit of $9 when you won (due to the cut that the bookmakers make per bet known as the vigorish, or vig) you would have profited (9x209) - (10x144) = $441 over that stretch. This is an extraordinary amount of money to be beating Vegas for when betting such a small amount per bet.

It is curious that while the model was seemingly totally ineffective against the spread, it performed superbly in regards to the point totals. One reason for this could be that the public was selecting the point total to go “over” at a highly disproportionate rate. In the analyzed sample of 343 games, the public chose the “over” 301/343 times, good enough for a 87.8% clip.

While the data collected offers no scientific explanation for this phenomenon, there is one possible easy explanation: conventional wisdom is that it is more entertaining to the casual observer to root for more points than for less points Many bettors are not betting “smart money”, or with data. They are instead betting for leisure. This simple explanation could be the reason that in our analyzed sample the public chose the “over” so many times, especially considering that our public consensus is based off of total number of bets, not total dollars bet.

14 Focused Analyses

A common betting strategy popular among gamblers has long been to “fade the public”

This is the action of betting the opposite of whatever the public consensus is for any given bet.

The rationale is as follows, described by bettingbrain.com:

“Sports gamblers like to fade the public simply because it’s logical to do so. After all, how could a sportsbook stay ​ in business if the gambling public won more than they lost? This is not a successful business model. Sportsbooks and oddsmakers always set the lines to get equal action on both sides of a wager. This way, they win all the losses and then collect the juice from the winners. However, when the gambling public load up one side, it’s the sportsbooks coming out ahead. Sportsbooks and their oddsmakers know what they’re doing, and their knowledge of the gambling public is what makes fading the public so logical. If the house is going to win, follow their lead.” 17 ​

This strategy seemed to be most applicable to total betting as the public is predisposed to betting the over at a disproportionate rate, a strategy that is certainly losing in the long term. This combined with the computer models success in this vein of betting as compared to against the spread. To test this strategy, we collected a separate group of data to analyze and test the hypothesis that fading the public is a viable strategy. The null hypothesis in this situation is that there would be no significant difference in correctly picking the over/under while fading the public. We collected eight more weeks of data in the NFL to test this hypothesis, from weeks one through eight of the 2016/2017 NFL season. This amounted to 115 games of which only the data on total betting was collected and the against the spread data was ignored. The results of this data are presented in the following two figures:

17 https://www.bettingbrain.com/strategy/sports-betting-strategy-when-and-why-to-fade-the-public/ ​

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Figure 9. Logistic Regression with new dataset and fadeTotal variable ​

Figure 10.Bar chart showing the amount of times fading the public would have won (represented as a 1) and how many times ​ they would have lost (represented as a 0) Starting with the regression, none of the variables are statistically significant including the fadeTotal variable. Thus, we fail to reject the null hypothesis of a statistically significant difference in correctly picking totals by fading the public. Further, looking at the bar chart of

16 the fadeCorrect variable we can see that over this eight week period, siding with the public was better than fading them. Remarkably, this is despite the fact that continuing with our observed trend the public tends to select the over at an extremely high rate. Throughout the sample, the public picked the over 96/115 times (83.4%). It is hard to draw any true conclusions from this sample about the strategy of fading the public. While this sample size is large (nearly half a season), it is hard to believe that selecting the over greater than 80% of the time will continue to be profitable. Likely, this was just an anomaly within the data and over a larger sample size this would not continue to occur. It would be interesting to see the result of an analysis of a larger amount of data to see if the null hypothesis could possibly be rejected. Within this larger study, it would be interesting to observe how a pure strategy of taking the under every game would fare.

If the data from Wunderdog is to be believed and the public truly takes the over greater than 80% of the time, oddsmakers would pick up upon this tendency and set over lines at artificially high numbers. Knowing this to be true, a smart bettor could put money on the under when he observes the total line trending in an upward direction, indicating public money has been put on the over.

Bucket Analysis

Following our final presentation in class, Professor Strumpf suggested that bucketing the public consensus data could lead to interesting conclusions about the data. Using Rstudio’s cut function, the data was binned into three separate “buckets” based on the strength of the public consensus. For our data, the three bins were as follows: “weak consensus” is defined as when the percentage of the public was between 50.1 and 61.3%, “some consensus” is defined as

17 between 61.4 and 72.7%, and strong consensus is defined as 72.7-84%. Results of this bucket analysis can been in the figures below.

Figures 11 and 12.Bar charts showing results of the bin analysis for both against the spread and total betting. In both charts, ​ the y axis is the number of observations recorded. The blue bar is the amount that was picked correctly in each case while the red bar is the amount picked incorrectly for the case of weak, some and strong consensus.

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Figures 13 and 14. Visualizations of logistic regressions showing the relationship between the public correctly choosing ​ against the spread or the total and the percent of the public that sided with the consensus

Figures 15 and 16.Output of logistic regression models looking for significance between percentage of people supporting the ​ consensus and getting the outcome of a game correct.

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The observed results of the bucket analysis show that the public’s level of consensus has no effect on predicting whether or not a selection will be accurate or not. As seen on the bar chart, when picking against the spread, the ratio of correct choices to incorrect choices does not seem to increase whatsoever as the public consensus grows stronger. Looking at the results of the regressions, we fail to reject the null hypothesis that fading the public will have no statistically significant increase in selecting the total or against the spread correctly with p-values in the model of 0.5902 and 0.2741 respectively. Further the pseudo R-squared of both models was

0.004, meaning that less than 1% of the variation in Y was explained by our model. While pseudo r-squared is not a perfect measure of model fit, when the value is this low combined with the p-value it shows a complete lack of relationship between the percent of the public which sided with the consensus and correctly selecting the over/under or spread winner. When picking totals, the amount of correct choices made by the public actually decreases as the consensus grows stronger. The regressions show the same thing, with a negative correlation (albeit, not a significant one) in the total regressions and a very slightly positive (although, once again not significant) in the against the spread regressions. It appears that according to our data the level

20 of public consensus, be it strong, some, or weak, has little to no effect at all on the probability of correctly selecting the total or the team to cover the spread.

Conclusions

This analysis has helped showed how the wisdom of the crowds operates in sports betting. In our project, the computer picks act as an expert and Vegas’ lines act as the crowd. The data recorded supports that Vegas creates a great betting line that also adjusts to the crowds prediction for the sports event. This makes the line difficult to consistently win against a reason against paying for “expert” picks. Although Wunderdog sports does offer paid picks that we did not utilize and may have been slightly better due to being more dynamic (factored in game-time decisions and injuries), it still would have performed worse than the crowd-influenced Vegas lines. This line acts as a crowd because it successfully adheres to James Surowiecki’s five rules for a crowd. Vegas’ bets are made up of a diverse group of opinions from various sports betters and those who create the lines. It has independence, no one is influencing each others’ bets. The line is decentralized as some are able to specialize and draw on local knowledge. The line is aggregated by turning private bets into a collective decision, which is the number of the line itself. And lastly, there is trust that the group will be fair and there won’t be cheating, or other ways for the game to be harmfully influenced.

The results of our models showed a lack of statistical difference between the experts and crowd predictions in point-spread betting versus bets placed using random selection of the favorite/underdog, but a very statistically significant (P<0.01) advantage of the computer

“expert” model over public consensus and random selection when selecting over/under bets.

21 Extended analysis comparing the results of the expert model and public consensus to the popular betting strategy of “fading the public” showed that there was no statistically significant difference between this strategy and these other two predictors. All of these results fail to support our hypothesis that the “public consensus” data would be a better predictor than Wunderdog’s

Computer Prediction Model or the strategy of “fading the public,” as none of the tests resulted in statistically significant advantages for the “public consensus.” As a result, we were unable to reject our null hypothesis that there would be no significant between difference the computer model and expert selections in choosing over/under and spread outcomes. As noted, the public exhibited a strong bias towards the “over” on these bets, so it is quite interesting that betting against the public is not a statistically more successful strategy. While the results of this project do not support Surowiecki’s argument that the aggregated knowledge of crowds is more valuable than expert predictions, there are a number of possible explanations for this result that do not invalidate Surowiecki’s claims. It is possible that this lack of significance can be attributed to the half-season sample size of this specific analysis, and uncertainty as to how Wunderdog aggregates their public consensus data and where they are getting the data from also could contribute to this lack of significance. Importantly, sports betting is different than many other possible uses of crowd wisdom. While it is relatively easy to obtain information about sports betting, by watching sports television, reading sports blogs, listening to “talking heads,” or subscribing to sports betting handicapping services such as Wunderdog, people often make bets that are either uninformed or contrary to the information they know. This is because of the influence of bias towards popular teams, historically successful franchises, and favorite-underdogs or up-and-comers. Additionally, as described earlier, often the public bets on

22 the “over” for over/under bets simply because higher-scoring games are considered by most to be more enjoyable and people want to bet in a way that allows them to cheer for the result they want to see.

For further research, it would be interesting to compare other aggregated public consensus data to this betting strategy and to Wunderdog’s Computer Prediction Model. It is possible that other providers would have different conclusions about the “public consensus” on bets. Additionally, as much of sports betting goes on illegally, it is possible that the “public consensus” of legal bets is different than the “public consensus” of all bets placed, legally and illegally. Another interesting area of research would involve using public consensus based not on number of bets placed but on total dollars bet. This would more accurately simulate a prediction market, as it would allow individuals to reflect their confidence in a bet by the amount of money that they are willing to bet. As this type of analysis would more accurately simulate a prediction market, it is likely that it would reflect a significant advantage for the “public consensus” over experts and other betting strategies, based on the conclusions of Surowiecki.

Appendix 1 - Variable Name Explanations

Week The week of games in the NFL that this game was observed

ATSResult The result of the observed game against the spread, 1 if the favorite covered 0 otherwise.

ATSComp The computers selection against the spread, 1 if the favorite 0 if the underdog.

PublicATS The public consensus selection against the spread, 1 if the favorite 0 if the underdog.

23 PctATS The percentage of the public that sided with the consensus pick

Total Result The result of the observed game in regards to the total 1 if the total went over, 0 otherwise.

ComputerTotal The computers selection against the total, 1 if the over 0 if the under.

PublicTotal The public consensus selection against the total, 1 if the over 0 if the under.

PctTotal The percentage of the public that sided with the consensus pick in regards to the total

Year The year the game was observed in fadeATS The opposite of what the public chose ATS fadeTotal The opposite of what the public picked in regards to the total

ATSCorrect A variable determining if the public was correct in choosing against the spread, a 0 if incorrect and a 1 if correct totalCorrectH A variable determining if the public was correct in choosing in regards to the total, a 0 if incorrect and a 1 if correct

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