Information, Forecasting and Corruption. James Reade.pdf · Information, Forecasting and Corruption...

Information, Forecasting and Corruption


J. James Reade

University of Reading

4th September 2014

15th OxMetrics User Group Meeting,Cass Business School


Outline

Introduction

Terminology

Theory I

Data and Results I

Theory II

Data and Results IIOverroundFavourite-Longshot Bias

Conclusions


Outline

I Problem: Corruption in sporting events;I Commonly match fixing (overall event) and spot fixing

(sub-events).I Distorts productive activity, creating huge economic rents.I Destroy economic value of sport: Uncertainty reduced.

I Economics of sport:I Exploiting data richness to learn about economic behaviour. . .I . . . and applying economic theories to context of sports.

I Aim: Use available data to understand/detect corrupt activity.

I Information allocation issue. Rent seekers vs producers.I Insiders place bets, introduces information into market.


Outline

I Various detection/investigation approaches:I Odds as forecasts. Different groups of forecasters have

different information.

1. Evaluate forecasts — not necessarily errors but discrepancies.

I Odds not necessarily as forecasts. Bookmaker behaviouralresponse to insiders.

2. Withdrawal of odds.3. Overround.4. Favourite-longshot bias.

I Main problem: No genuine information on corrupt sportingevents.

I Spillover(s):I Collection of massive dataset of dynamic pricing behaviour.I ‘Conventional’ study of forecast accuracy.


Terminology

Terminology

I Forecasts: Prices (odds) from bookmakers, tips from punters,forecasts from econometric models.

I Events (j): Team 1 (usually at home) to win, draw, team 2(usually visiting team) to win for a given football match (i).

I Traditional odds: Oij , winnings for £1 stake, j = 1, 2, 3.

I Decimal odds: Dij , winnings inclusive of £1 stake.

I Overround: ORi =∑3

j=1D−1ij .

I Implied probability: Pij = (DijORi )−1.

I Favourite longshot bias (FLB): Expected return increasing inPij .


Theory I

I Numerous forecasts of same event:

1. Bookmakers.2. Prediction markets.3. Tipster predictions.4. Econometric model.

I Significant disagreement between forecasts implies one (ormore) of:

I Different information sets.I Different forecast models process information differently (or

not at all).


Theory I

I Previous research: Discrepancy between (1) and (4).I Plot from Reade (2013)

I Briefly today:I Focus on bookies, prediction markets and tipsters.I Disagreement: Standard deviation of implied probabilities.I Investigate withdrawal of prices by bookmakers.

I Problems:I Numerous alternative explanations for differences.I Insufficient outcome data on which to appraise method.


Data and Results I

I Focus on international matches following 2013 Interpol pressconference.

I 13078 matches scraped from www.oddsportal.com/soccer/.I Screenshot of data source

I 62 bookmakers, 3 prediction markets, set of tipsters (websiteusers).

I 4,423 matches by tipsters.I 30.7 bookmakers per match.I 3 prediction markets per match.

http://www.oddsportal.com/soccer/


Data and Results I

DisagreementI Standard deviation of all outcome predictions by:

I Event (home win, draw, away win) and match popularity.

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0 10 20 30 40 50 60

0.0

0.1

0.2

0.3

0.4

Bookmaker disagreement over offered odds

Number of bookmakers offering prices for a match

Dis

agre

emen

t

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https://www.dropbox.com/s/fjnjjwuumic0olj/disagreement_prices.pdf?dl=0


Data and Results I

But little agreement on matches

38

18

48

36

8

26

30

Home Draw

Away


Data and Results I

Withdrawn Prices Plot

I Bookmakers withdraw prices in days before event occurs:Why?

I Event foregone conclusion.I Event known to be fixed (hence also foregone conclusion).

I 13078 matches:I 4560 have no withdrawals.I 33 matches see all bookmakers withdraw odds.I Half of all matches have 4% or fewer bookies withdrawing.

I High disagreement matches and 33 are:I Friendlies, youth matches and womens’ matches.I No ‘important’ matches.I Corroborates Reade and Akie (2013) findings.


Theory II

Theory II

I Betting industry simplified version of financial markets:I Short time horizon, simple observable outcomes.I Bookmakers and betting exchanges act as intermediaries.

I Overround: Transactions cost — cost of £1 return.I Margin for bookmakers, expect competed down to operating

costs by competitive market.

I FLB: Various explanations, including:I Risk love: Lower returns for higher risk.I Information: Bookmakers influence uninformed bettors.I Misperceptions: Bettors cannot distinguish between low

probability events.

I Shin (1991, 1992, 1993): Influence of insider trading onbetting market outcomes.

I Drives both overround and favourite-longshot bias.


Theory II

More on Shin

I Insider information exists when markets open.

I Insider knows which horse will win.I Bookmakers must price accordingly:

I Larger overround and FLB.I FLB should increase absolute/squares forecast errors.

I What if insiders appear after market opens?I Shin: Comparative statics, no dynamics.

I Movements in overround and FLB?I Information set increases (more bookmakers post prices)?I Or response to specific information?


Data and Results II

I Expanded dataset of bookmaker and betting exchange prices:I 4682 matches, 4.9m observations; 1048 per match. Screenshot

I All prices, times of updates, if/when withdrawn, indication ofcollection problems.

I On average, 44 bookmakers per match (maximum 75, total80).

Total Prices Posted by Bookmakers in Regions

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south−africasouth−america

south−koreasweden

switzerlandthailandukraine

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● ● ●Less than 111 Between 111 and 503 Greater than 503

Larger Graphic


Data and Results II

Data Plot LA Galaxy vs SJ Earthquakes Rosario vs Quilmes

Jun 24 Jun 29 Jul 04 Jul 09 Jul 14

0.30

0.35

0.40

0.45

0.50

Prices for Criciuma vs Fluminense, Brazilian Serie A

Time/Date

Impl

ied

Pro

babi

lity

Criciuma to win Draw Fluminense to win


Data and Results II

Lots of Prices Withdrawn

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0.2

0.4

0.6

0.8

Prices for Mariehamn vs Rovaneimi, Veikkausliiga, Finland

Time/Date

Impl

ied

Pro

babi

lity

Mariehamn to win Draw Rovaneimi to win


Data and Results II

Questions we can ask...?

I How often are prices adjusted?

I More frequent adjustments with bookmaker activity, nearkick-off?

I Specific to corruption context:I Overround.I Favourite-longshot bias/Forecast errors.

I Anticipate both larger for many reasons including corruption.I Early steps: Model both. To do: Residual analysis.


Data and Results II

Overround

Overround Regression Model Overround Plot

orit = β0 + β1Nbookiesit + β2pricesupdatedit

+ β3entropyit + β4tKOit + ui + εit .(1)

I Fixed effects for operating costs.I Competition: Number of bookies posting odds.

I Entropy to measure competitiveness of match (by bookie k):

Hik = −∑j

Pijk log(Pijk) (2)

I Range between 0 (Pijk ∈ {0, 1}) and 1.1 (Pijk = 1/3 ∀ j).

I Information arrival: Number of updates to odds.

I Remaining time variation? SIS?


Data and Results II

Overround

Overround Regression Model Full Output Overround Plot

Dependent variable:

Overround

Constant 1.110∗∗∗

(0.0002)

time to KO −0.000∗∗∗

(0.000)

bookies posted −0.0001∗∗∗

(0.00000)

obsnum −0.00001∗∗∗

(0.00000)

entropy 0.001∗∗∗

(0.0001)

Observations 2,861,779R2 0.359Adjusted R2 0.359Residual Std. Error 0.028 (df = 2861699)F Statistic 20,327.820∗∗∗ (df = 79; 2861699)F Statistic (fixed effects) 16,580∗∗∗ (df = 75; 2861699)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01


Data and Results II

Overround

Overround Regression

I Operating costs/efficiency: Fixed effects highly significant.

I Competition: Overround falls 0.01% per bookie posting; lowerby 0.75% if all post.

I News: Each update reduces overround by 0.001%.1

I Time until kick-off: Overround higher nearer kick-off. 0.07%per 24 hours.2

I Decidedness: Tighter contest, higher overround.

I What remains?

150% of matches have more than 500 updates, 18% more than 1000.290% of observations within 3 days of kick-off.


Data and Results II

Favourite-Longshot Bias

Testing for Favourite-Longshot Bias (FLB)

I One common test for FLB is Mincer-Zarnowitz regression test:

yij = β0 + β1yij + εij . (3)

I Embodies two ideas: E (yij) = E (yij) andCov(yij , yij) = Var(yij).

I Blunt instrument for learning characteristics of bias:I Like Sinclair et al. (2010) augment with dummies.


Data and Results II


FLB Regression Summary Full Output Plot (time) Plot (overall)

Dependent variable: outcome

α β

Coefficient 0.067∗∗∗ 0.862∗∗∗

(0.006) (0.013)

time to KO 0.00000∗∗∗ −0.00000∗∗∗

(0.000) (0.000)

bookies posted −0.001∗∗∗ 0.001∗∗∗

(0.0001) (0.0001)

obsnum 0.00004∗∗∗ −0.0001∗∗∗

(0.00000) (0.00000)

entropy −0.103∗∗∗ 0.180∗∗∗

(0.006) (0.011)

Observations 2,861,779R2 0.119Adjusted R2 0.119Residual Std. Error 0.460 (df = 2861619)F Statistic 2,436.754∗∗∗ (df = 159; 2861619)F Statistic (fixed effects) 14.281∗∗∗ (df = 150; 2861619)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01


Data and Results II


FLB Regression

I Fixed effects (slope also): Significant (pushing into FLB).

I Decidedness: Will generally push into FLB territory.

I Competition: Significant impact on both coefficients.

I Information: Updating of competitors impacts FLB.


Conclusions

Conclusions

I Corruption big problem in many sports.

I Must affect expected outcomes — but where is information?I Focus on betting markets:

I Fixers must bet to profit from activity.I Betting markets appear to be efficient from other research.

I Basic research suggests that non-important matches appearsuspect.

I Ongoing research: More data = more insight?

I Further research: IIS/SIS/etc may help detect matches.


Appendix

Back to slide


Appendix

Back to slide


Appendix

Total Prices Posted by Bookmakers in Regions

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Appendix

Data Plot II Go back Rosario vs Quilmes

●● ●● ●●●● ●●●●●● ●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●● ●●●●●●●● ●●●● ●●●●●●●●●● ●● ●●●● ●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●● ●●●●●● ●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●● ●●●● ●●●●●●●●●●●● ●●●● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●● ●●●●●● ●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●● ●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

Aug 02 Aug 04 Aug 06 Aug 08

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Prices for LA Galaxy vs San Jose Earthquakes, MLS

Time/Date

Impl

ied

Pro

babi

lity

LA Galaxy to win Draw SJ Earthquakes to win


Appendix

Data Plot III Go back LA Galaxy vs SJ Earthquakes

Jul 29 Aug 03 Aug 08

0.2

0.3

0.4

0.5

0.6

Prices for Rosario Central vs Quilmes, Argentinian Primera Division

Time/Date

Impl

ied

Pro

babi

lity

Rosario Central to win Draw Quilmes to win


Appendix

0.0

0.2

0.4

0.6

0.8

1.0

Kaplan−Meier estimate with 95% confidence bounds

Days

surv

ival

func

tion

12bet

188bet

5dimes

888sport

bestbet

bet2be

bet365

bet−at−home

betclic

betclic−it

betcris

betdaq

betfairbetfred

betgun

betonline

betrallybetredkings

betsafebetsson

betvictor

betway

bovada

boylesports

bwinbwin−es

bwin−fr

bwin−it

coral

dafabet

doxxbet

expekt

fortuna−cz

fortunawin

france−pari

gobetgo

intertops

interwetteninterwetten−es

island−casinojetbull

marathonbet

mybet

nordicbet

noxwin

paddy−power

paddy−power−it

paf

pinnacle−sportssbobet

sportingbet superbahis

tipicotipico−it

tipsport−cz

unibet

unibet−frunibet−it

0 1 2 3 4 5 6 7 8 9 10

Go back


Appendix

0.0

0.2

0.4

0.6

0.8

1.0

Kaplan−Meier estimate with 95% confidence bounds

Days

surv

ival

func

tion

>1000 p changes in hour=0, 100 mkt p chances in hour=0, <6h to KO=0>1000 p changes in hour=0, 100 mkt p chances in hour=1, <6h to KO=0>1000 p changes in hour=1, 100 mkt p chances in hour=0, <6h to KO=0>1000 p changes in hour=1, 100 mkt p chances in hour=0, <6h to KO=1>1000 p changes in hour=1, 100 mkt p chances in hour=1, <6h to KO=0>1000 p changes in hour=1, 100 mkt p chances in hour=1, <6h to KO=1

0 1 2 3 4 5

Go back


Appendix

Withdrawn Prices Go back

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0 10 20 30 40 50

010

2030

4050

60

Bookmakers offering and withdrawing odds

Number of bookmakers withdrawing prices for a match

Num

ber

of b

ookm

aker

s of

ferin

g pr

ices

for

a m

atch


Appendix

Go backDependent variable:

o.r

Constant 1.110∗∗∗

(0.0002)

time to KO −0.000∗∗∗

(0.000)

bookies posted −0.0001∗∗∗

(0.00000)

obsnum −0.00001∗∗∗

(0.00000)

entropy 0.001∗∗∗

(0.0001)

12bet 0.0004∗∗∗

(0.0001)

188bet 0.003∗∗∗

(0.0002)

5dimes −0.047∗∗∗

(0.0001)

888sport 0.0002(0.0002)

bestbet −0.010∗∗∗

(0.0001)

bet2be 0.005∗∗∗

(0.0002)

bet365 −0.014∗∗∗

(0.0002)

bet-at-home 0.018∗∗∗

(0.0002)

betclic 0.004∗∗∗

(0.0001)

betclic-it 0.001∗∗∗

(0.0002)

betcris −0.031∗∗∗

(0.0003)

betdaq −0.037∗∗∗

(0.0001)

betfred −0.004∗∗∗

(0.0002)

betgun −0.004∗∗∗

(0.0003)

betonline −0.009∗∗∗

(0.0002)

betrally −0.006∗∗∗

(0.0001)

betredkings −0.002∗∗∗

(0.0001)

betsafe 0.007∗∗∗

(0.0002)

betsson 0.009∗∗∗

(0.0002)

betvictor −0.029∗∗∗

(0.0001)

betway 0.004∗∗∗

(0.0002)

bovada −0.005∗∗∗

(0.001)

boylesports 0.011∗∗∗

(0.0002)

bwin 0.002∗∗∗

(0.0002)

bwin-es 0.002∗∗∗

(0.0002)

bwin-fr 0.055∗∗∗

(0.0004)

bwin-it −0.002∗∗∗

(0.0002)

coral 0.001∗∗∗

(0.0003)

dafabet 0.0001(0.0001)

doxxbet 0.011∗∗∗

(0.0002)

expekt 0.004∗∗∗

(0.0001)

fortuna-cz 0.003∗∗∗

(0.0002)

fortuna-sk 0.005∗∗∗

(0.0002)

fortunawin 0.003∗∗∗

(0.0002)

france-pari 0.075∗∗∗

(0.0004)

gobetgo −0.010∗∗∗

(0.0002)

instant-action-sports 0.011∗∗∗

(0.001)

intertops 0.007∗∗∗

(0.0003)

interwetten 0.026∗∗∗

(0.0002)

interwetten-es 0.026∗∗∗

(0.0002)

island-casino −0.047∗∗∗

(0.0001)

jetbull −0.010∗∗∗

(0.0001)

leon-bets 0.007∗∗∗

(0.0001)

luxbet 0.009∗∗∗

(0.0002)

marathonbet −0.054∗∗∗

(0.0002)

matchbook −0.077∗∗∗

(0.0002)

mybet −0.004∗∗∗

(0.0002)

nordicbet −0.001∗∗∗

(0.0002)

noxwin −0.010∗∗∗

(0.0001)

oddsring −0.008∗∗∗

(0.0001)

paddy-power 0.009∗∗∗

(0.0002)

paddy-power-it 0.011∗∗∗

(0.0002)

paf −0.002∗∗∗

(0.0002)

pinnacle-sports −0.066∗∗∗

(0.0002)

sbobet −0.002∗∗∗

(0.0001)

sportingbet 0.011∗∗∗

(0.0002)

sportium 0.018∗∗∗

(0.001)

sportsbetting −0.009∗∗∗

(0.0002)

stan-james −0.019∗∗∗

(0.0002)

superbahis 0.011∗∗∗

(0.0002)

tipico −0.007∗∗∗

(0.0002)

tipico-it −0.007∗∗∗

(0.0002)

tipsport-cz −0.004∗∗∗

(0.0002)

tipsport-sk −0.004∗∗∗

(0.0002)

titanbet −0.002∗∗∗

(0.0002)

tonybet −0.014∗∗∗

(0.0003)

top-bet 0.013∗∗∗

(0.002)

totepool −0.002∗∗∗

(0.0001)

unibet −0.001∗∗∗

(0.0002)

unibet-fr 0.051∗∗∗

(0.0002)

unibet-it −0.009∗∗∗

(0.0003)

william-hill 0.004∗∗∗

(0.0002)

william-hill-it −0.001∗∗∗

(0.0002)

winner −0.002∗∗∗

(0.0002)

youwin 0.014∗∗∗

(0.0002)

Observations 2,861,779R2 0.359Adjusted R2 0.359Residual Std. Error 0.028 (df = 2861699)F Statistic 20,327.820∗∗∗ (df = 79; 2861699)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01


Appendix

Overround as kick-off approaches OR Overview OR Regression

Over−round at kick−off time and days beforehand (by bookmaker)

Ove

r−ro

und

pinn

acle

−sp

orts

isla

nd−

casi

no5d

imes

mar

atho

nbet

mat

chbo

okbe

tcris

betv

icto

rto

nybe

tbe

tdaq

stan

−ja

mes

spor

tsbe

tting

beto

nlin

eje

tbul

lbe

tral

lybe

stbe

tno

xwin

bet3

65tip

spor

t−sk

tipsp

ort−

czun

ibet

−it

odds

ring

bova

dabe

tgun

bwin

−it

tipic

oco

ral

tipic

o−it

gobe

tgo

dafa

bet

12be

tno

rdic

bet

888s

port

top−

bet

titan

bet

win

ner

myb

et paf

betfr

edbe

tclic

−it

inst

ant−

actio

n−sp

orts

tote

pool

will

iam

−hi

ll−it

unib

etsp

ortiu

mfo

rtun

a−sk

sbob

etbo

yles

port

sfo

rtun

a−cz

fort

unaw

inw

illia

m−

hill

inte

rtop

sex

pekt

bwin

−es

betr

edki

ngs

luxb

etbe

tway

betc

licbw

inbe

t2be

188b

etbe

tsaf

ele

on−

bets

padd

y−po

wer

padd

y−po

wer

−it

doxx

bet

bets

son

spor

tingb

etsu

perb

ahis

unib

et−

fryo

uwin

bet−

at−

hom

ein

terw

ette

n−es

bwin

−fr

inte

rwet

ten

fran

ce−

pari

5%

10%

15%

−40

0−

300

−20

0−

100

010

0

t−st

atis

tics

Kick−off24 hours before48 hours before72 hours before96 hours before120 hours before


Appendix

Go backDependent variable: outcome0

α β

Coefficient 0.067∗∗∗ 0.862∗∗∗

(0.006) (0.013)

time to KO 0.00000∗∗∗ −0.00000∗∗∗

(0.000) (0.000)

bookies posted −0.001∗∗∗ 0.001∗∗∗

(0.0001) (0.0001)

obsnum 0.00004∗∗∗ −0.0001∗∗∗

(0.00000) (0.00000)

entropy −0.103∗∗∗ 0.180∗∗∗

(0.006) (0.011)

12bet −0.004 0.048∗∗∗

(0.006) (0.015)

188bet −0.002 0.028(0.008) (0.017)

5dimes 0.022∗∗∗ −0.005(0.007) (0.015)

888sport 0.013 0.068∗∗∗

(0.010) (0.023)

bestbet −0.011∗∗ 0.031∗∗∗

(0.005) (0.011)

bet2be 0.024∗∗∗ 0.005(0.009) (0.021)

bet365 −0.002 0.008(0.008) (0.019)

bet-at-home −0.0004 0.025∗

(0.007) (0.015)

betclic 0.008 0.002(0.006) (0.013)

betclic-it −0.003 0.048∗∗

(0.009) (0.019)

betcris 0.003 0.070∗∗

(0.013) (0.029)

betdaq 0.012∗ 0.004(0.006) (0.013)

betfred 0.009 0.008(0.007) (0.016)

betgun 0.006 0.091∗∗∗

(0.013) (0.029)

betonline −0.002 0.087∗∗∗

(0.012) (0.026)

betrally 0.003 −0.008(0.005) (0.011)

betredkings −0.007 0.032∗∗∗

(0.005) (0.011)

betsafe −0.014 0.094∗∗∗

(0.009) (0.020)

betsson −0.017∗ 0.103∗∗∗

(0.009) (0.020)

betvictor −0.002 0.016(0.006) (0.012)

betway 0.006 0.092∗∗∗

(0.009) (0.021)

bovada 0.015 −0.087(0.028) (0.058)

boylesports 0.002 −0.015(0.013) (0.027)

bwin 0.007 0.031∗

(0.008) (0.019)

bwin-es 0.005 0.043∗∗

(0.008) (0.019)

bwin-fr −0.047∗∗∗ 0.105∗∗∗

(0.017) (0.037)

bwin-it 0.029∗∗∗ −0.033(0.010) (0.023)

coral 0.052∗∗∗ −0.075∗

(0.019) (0.042)

dafabet −0.003 0.043∗∗∗

(0.007) (0.015)

doxxbet 0.015 0.068∗∗∗

(0.010) (0.023)

expekt 0.008 0.0003(0.006) (0.013)

fortuna-cz −0.019∗ 0.101∗∗∗

(0.011) (0.024)

fortuna-sk −0.024∗∗ 0.112∗∗∗

(0.011) (0.025)

fortunawin −0.022∗∗ 0.112∗∗∗

(0.011) (0.024)

france-pari −0.042∗∗ 0.130∗∗∗

(0.019) (0.040)

gobetgo −0.006 0.013(0.009) (0.019)

instant-action-sports 0.085∗∗ −0.008(0.034) (0.074)

intertops 0.029∗ 0.017(0.016) (0.036)

interwetten 0.017 0.059∗∗

(0.011) (0.025)

interwetten-es 0.014 0.071∗∗∗

(0.011) (0.025)

island-casino 0.019∗∗∗ 0.003(0.007) (0.015)

jetbull −0.011∗∗ 0.032∗∗∗

(0.005) (0.011)

leon-bets 0.012∗ 0.015(0.007) (0.015)

luxbet 0.005 0.016(0.007) (0.016)

marathonbet −0.006 0.0003(0.009) (0.019)

matchbook 0.051∗∗∗ −0.122∗∗∗

(0.011) (0.022)

mybet 0.022∗∗ 0.026(0.009) (0.020)

nordicbet 0.042∗∗∗ −0.032(0.011) (0.023)

noxwin −0.010∗ 0.030∗∗∗

(0.005) (0.011)

oddsring 0.015∗∗ 0.010(0.007) (0.014)

paddy-power 0.018∗∗ 0.012(0.009) (0.020)

paddy-power-it 0.018∗∗ 0.007(0.009) (0.020)

paf 0.011 0.048∗∗

(0.009) (0.019)

pinnacle-sports 0.032∗∗∗ −0.054∗∗∗

(0.007) (0.015)

sbobet −0.018∗∗∗ 0.076∗∗∗

(0.006) (0.014)

sportingbet 0.005 0.009(0.009) (0.020)

sportium 0.053 −0.228∗∗∗

(0.044) (0.083)

sportsbetting −0.012 0.103∗∗∗

(0.012) (0.026)

stan-james −0.009 0.027(0.011) (0.023)

superbahis 0.004 0.018(0.009) (0.020)

tipico 0.024∗∗∗ −0.014(0.009) (0.019)

tipico-it 0.026∗∗∗ −0.020(0.009) (0.019)

tipsport-cz −0.012∗ 0.056∗∗∗

(0.007) (0.016)

tipsport-sk −0.015∗∗ 0.061∗∗∗

(0.007) (0.016)

titanbet 0.019∗∗ −0.028∗

(0.007) (0.016)

tonybet 0.066∗∗∗ −0.088∗∗∗

(0.015) (0.032)

top-bet 0.320∗∗ −0.361(0.128) (0.245)

totepool 0.002 0.043∗∗∗

(0.007) (0.016)

unibet 0.022∗∗ 0.059∗∗∗

(0.010) (0.021)

unibet-fr −0.099∗∗∗ 0.196∗∗∗

(0.008) (0.018)

unibet-it −0.015 0.089∗∗∗

(0.016) (0.035)

william-hill −0.003 0.050∗∗

(0.011) (0.024)

william-hill-it −0.003 0.029(0.011) (0.025)

winner 0.024∗∗∗ −0.038∗∗

(0.007) (0.016)

youwin 0.002 0.037∗∗

(0.008) (0.017)

Observations 2,861,779R2 0.119Adjusted R2 0.119Residual Std. Error 0.460 (df = 2861619)F Statistic 2,436.754∗∗∗ (df = 159; 2861619)

Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01


Appendix

FLB as kick-off approaches Go back1.

01.

52.

0

Beta coefficient at kick−off time and days beforehand (by bookmaker)

beta

coe

ffici

ents

in F

LB m

odel

top−

bet

spor

tium

mat

chbo

okin

stan

t−ac

tion−

spor

tsbo

vada

unib

et−

frbo

yles

port

sco

ral

stan

−ja

mes

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Implied probability of event

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● Home winDrawAway win


Appendix

Model and Bookmaker Forecasts from Reade and Akie(2013) Go back

Bookmaker Implied Probabilies Model probabilities (fitted values)

150 300 450 600 750 900 1050 1200 1350 1500 1650 1800

0.25

0.50

2011-20122010-20112009-20102008-2009

Bookmaker Implied Probabilies Model probabilities (fitted values)


References

J.J. Reade. Detecting corruption in football. In J. Goddard andP. Sloane, editors, Handbook on the Economics of ProfessionalFootball. Edward Elgar, 2013.

J.J. Reade and S. Akie. Using Forecasting to Detect Corruption inInternational Football. Working Papers 2013-005, The GeorgeWashington University, Department of Economics, ResearchProgram on Forecasting, May 2013. URLhttp://ideas.repec.org/p/gwc/wpaper/2013-005.html.

H.S. Shin. Optimal Betting Odds Against Insider Traders. TheEconomic Journal, 101(408):1179–1185, September 1991.

H.S. Shin. Prices of State Contingent Claims with Insider Traders,and the Favourite-Longshot Bias. The Economic Journal, 102(411):426–435, March 1992.

H.S. Shin. Measuring the Incidence of Insider Trading in a Marketfor State-Contingent Claims. The Economic Journal, 103(420):1141–1153, September 1993.

http://ideas.repec.org/p/gwc/wpaper/2013-005.html


Appendix

Tara M. Sinclair, Fred Joutz, and H.O. Stekler. Can the Fedpredict the state of the economy? Economics Letters, 108(1):28–32, July 2010. URL http://ideas.repec.org/a/eee/

ecolet/v108y2010i1p28-32.html.

http://ideas.repec.org/a/eee/ecolet/v108y2010i1p28-32.html

http://ideas.repec.org/a/eee/ecolet/v108y2010i1p28-32.html

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