ox ac paper on sporting news efficiency

8/7/2019 OX AC paper on sporting news efficiency

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Information and Efficiency: Goal Arrival in Soccer Betting

Karen Croxson and J. James ReadeDepartment of Economics, Oxford University

[email protected], [email protected]

This Version: November 2007

In an efficient market news is incorporated into prices rapidly and completely. At-tempts to test for this in nancial markets have been undermined by the possibilityof information leakage unobserved by the econometrician. An alternative is to switchto laboratory conditions, at the price of some articiality. Potentially, sports bettingmarkets offer a superior way forward: traded assets have terminal values and newscan break remarkably cleanly, as when a goal is scored in soccer. We exploit this con-text to test for efficiency, applying a novel identication strategy to high-frequencydata. On our evidence, prices swiftly and fully update.

1 We are grateful to several colleagues for their interest in and advice regarding this research, particularly PeteKyle and Kevin Sheppard. Versions of the paper were presented at the Oxford-Man Institute of QuantitativeFinance, Oxford Universitys Postdoc and D.Phil. Workshop in Economics, the Conference in Honour of David Hendry, Oxford, 2007, and the IMAs First International Conference on Mathematical Modelling inSport, 2007. We received many helpful comments at these events. Financial assistance from the Economicand Social Research Council and the Economics Department at Oxford is also gratefully acknowledged.


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I. Introduction

A matter of considerable importance in economics and nance is how relevant information

becomes impounded in market prices. The signicance of the topic derives partly from

its theoretical pertinence: the efficient functioning of the price mechanism requires that a

securitys price at all times reect its true fundamental value. It also has much to do with

practical interests: traders with superior information may secure gains at the expense of

the less well informed. Encouragingly, the efficient markets hypothesis predicts that asset

prices will incorporate relevant information, and, in the simplest interpretation, will do so

immediately and completely .2 Extensive efforts have been made to put matters to the test.Fama (1970) popularized the idea of considering efficiency in relation to subsets of the totality

of information, focusing on three differently stringent tests. The rst and most lenient test is

for weak form efficiency, which requires the current price to reect all information contained

in historical prices. 3 A second test deals with semi-strong form efficiency. In a market that

is semi-strong form efficient prices completely and immediately update to new information,

provided that this very obviously is publicly available .4 Finally, and most stringently, there

is the notion of strong form efficiency. For a market to be efficient in the strong form sense,price must at all times reect all available information, even where this is held privately .5

Applying efficiency tests in the real world, most investigations have centered on conventional

nancial markets. 6 For instance, and regarding public information (the second form of 2 The efficient markets hypothesis is most commonly associated with Eugene Fama (1965, 1970, 1991). Itsearly origins can be traced back to the work of Louis Bachelier, who in 1900 studied the dynamics of stockprice behavior.3 For illustration, weak-form efficiency rules out the possibility that technical analysis techniques could beused to produce excess returns, though analysis of fundamentals still might.4 By implication, under semi-strong form efficiency not only technical analysis but also fundamental analysiswill be powerless to deliver abnormal returns. With regard to the speed and completeness of updating,the denition of semi-strong form efficiency given in the text is the strictest interpretation. Less strictformulations exist whereby it is sufficient for efficiency that it not be possible to trade upon the relevantsubset of information in such a way as to earn above-normal prots.5 Strong form efficiency suggests that no one can earn excess returns, not even with privileged information.6 Vaughan-Williams (2005) offers a comprehensive review of the academic literature which has investigatedinformation efficiency in nancial markets.


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test), researchers have scrutinized the response of share prices to such things as stock splits

(Fama et al. 1969), the release of company results (Ball and Brown 1968, Beaver 1968),

merger announcements (Asquith 1983), and announcements about economic variables such

as the money supply (Waud 1970, Chen et al. 1986). Somewhat problematically considering

the objective of such enquiries, it can be hard to tell when news really breaks in nancial

marketsit is difficult to rule out information leakage not observed by the econometrician. 7

Some investigators have preferred to analyze markets in the laboratory, where conditions

can be tightly controlled (Chamberlain 1948; and more recently Plott and Sunder 1988; List

2004). But while experimental settings can eliminate some concerns their articiality raises

others: What trading experience do subjects have? Are they appropriately motivated? 8

Potentially, sports betting markets offer a superior lens for efficiency studies, especially where

news arrival is the focus. Unlike laboratory experiments, these are real markets, with par-

ticipants that are properly motivated and often experienced. Contracts on sports outcomes

have well-dened terminal values (unlike equities and other nancial securities) and this helps

simplify analysis. 9 Moreover, and most importantly, major sports news often breaks remark-

ably cleanly. For instance, once a soccer game has kicked off the most signicant innovationin information concerns the scoring of a goal, and this event becomes common knowledge

at a single identiable point in time. This is particularly so where a game is televised, as

many now are. Unfortunately, until very recently it was impractical to base efficiency stud-

ies around sports news; wagering was tightly controlled by traditional bookmakers (dealers),

who posted prices, updated these infrequently, and allowed betting only up until kick-off.

Only in 2000 did this picture change, but the change when it came was dramatic: harnessing

7 Some market participants may be party to (some part of) the content of announcements before these gopublic (Jarrell and Poulson 1989). See Worrell et al. (1991) for an illustrative discussion of leakage in thecontext of layoff announcements.8 See Levitt and List (2006) for a recent consideration of factors affecting the generalizability of laboratoryndings, including the extent to and manner in which subjects are scrutinized in their decision-making.9 As Gil and Levitt (2006) explain, in the context of sports betting there is no need to take account of expectations regarding future events (e.g. dividend streams) when pricing the asset.


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the Internet and inspired by conventional nancial exchanges, betting exchanges began to

appear, offering online order-driven markets in bets. For the rst time, prospective punters

could view a live order book, buy and sell bets at prevailing prices, submit limit orders, and

do all this in-running (as play unfolds). The development proved popular with customers,

and, in a few short years, the leading exchanges established themselves as serious operations.

The dominant player, Betfair, already sees trading comparable in intensity (if considerably

smaller in volume) to activity on the worlds leading nancial exchanges.

Recognizing the research opportunity exchange betting presents, Gil and Levitt (2006) ana-

lyze data from the Intrade exchange (www.intrade.com), which operated markets for sports-

related bets until recently. 10 Considering fty matches from the 2002 Soccer World Cup, the

authors report that Intrade prices, though they respond strongly to a team scoring, trend

for ten to fteen minutes after the goal is registered. On the face of it, this drift appears

reminiscent of the post-news drift found in some nancial market studies. The nance litera-

ture describes post-event drift as the tendency of individual stocks performances following

major corporate news events to persist for long periods in the same direction as the return

over a short windowusually one to three daysencompassing the news announcement it-

self (Jackson 2006). 11 Gil and Levitt interpret the drift they observe in Intrades markets

as evidence of informational inefficiencyprices, they suggest, update sluggishly to the news

of a goal. Unfortunately, though their analysis is insightful in many ways (for one, the data

come disaggregated at the individual trader level, and the authors are able to document

10 To avoid difficulties with US law, which considers wagering on sports to be gambling, Intrade-Tradesports,

a single operator at the time of the 2002 World Cup, has since split into two separately registered companieswith different activities: Tradesports deals with sports betting, whereas Intrade now operates as a predictionmarket focused exclusively on non-sports events.11 The literature concerning price drift after various corporate events is reviewed by Kothari and Warner(1997), Fama (1998), and Daniel, Hirshleifer, and Subrahmanyam (1998). To mention just two studies,Patell and Wolfson (1984) nd that following dividend and earnings announcements it takes ve to tenminutes for protable trading opportunities in individual equities to disappear, and Chan (2003) examinesreturns to a subset of stocks after public news about them is released and nds evidence of post-news drift.


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the endogenous emergence of market makers), as a test for semi-strong form efficiency it is

limited. There are two reasons for this.

The rst relates to data quality: Intrade soccer markets attract very few traders (on average

just 75 per game); these people make very infrequent trades (an average game attracts 100

200 trades and features several minutes in which trades do not occur); and betting volumes

typically are low (just $1.5m is traded in total across the full set of fty matches).

The second limitation concerns methodological approach. Gil and Levitt interpret as ev-

idence against efficiency the fact that prices in these soccer markets drift for some time

following a goal. But, crucially, price drift could be evidence for efficiency in sports markets

that are in play. Consider that, once a soccer match (or other timed sports encounter) is

underway, participants update to major news, such as a goal, but also continually update

to the passage of time without a goal. Merely the ticking down of the clock gives rise to a

continual ow of information, and so prices should continually drift. Depending on the phase

of play considered, the current score, and probably many other factors, purely time-related

drift (evidence for efficiency) could be substantial, and properly identifying goal-related drift

(evidence of inefficiency) in its presence is likely to be a non-trivial affair. The authors ac-

knowledge (p.7, footnote 7) that playing time without a goal constitutes minor news, but,

from their analysis and discussion of results, they do not perceive this to present a serious

complication. We suggest, to the contrary, that the identication issue must be resolved, if

proper inferences are to be drawn regarding informational efficiency.

In this paper, we deploy a new data set to test for semi-strong form efficiency and propose a

simple identication strategy to deal with time-related drift. Our data were extracted second-by-second, from the live order book of the Betfair exchange (www.betfair.com). Betfair

arrived onto the market in 2000, becoming one of the very rst betting exchanges. It has

grown to be overwhelmingly the largest; its turnover of $50m per week accounts for ninety

per cent of all exchange-based betting activity worldwide and it currently has over a million


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registered users. Two million trades a daysix times the number of trades on the London

Stock Exchangeare processed through Betfair markets, which now cover a vast variety of

events, mostly sporting .12 Our data consist of in-running prices and volumes related to

betting on the outcomes of English Premiership soccer matches played during the 200607

season. These markets are particularly heavily traded (on average $6m is traded per game).

Our identication strategy is notably simple but attractively clean. It involves exploiting

the (virtually) newsless window provided naturally by the fteen minute half-time interval.

Since the clock stops during this break in play, time-related drift is not possible and the

matter of identifying potential goal-related drift is drastically simplied. Concretely, westudy matches with goals that arrive on the cusp of half-timethere are thirty nine in our

sampleand test the hypothesis that such news immediately shifts prices but does not cause

these to drift during the interval. We are unable to reject this hypothesis: prices update

so swiftly and completely that the news of a goal is fully digested by the time the break

commences. This is so even where the goal occurs just moments before the end of play. On

this evidence, we suggest that these markets are remarkably semi-strong form efficient.

We can think of two possible concerns with the approach we adopt, both related to thepotential for our efficiency nding to be an artefact of our half-time identication strategy.

First, one might suspect that our inability to detect sluggishness in updating is due primarily

to a lull in trading over the breakperhaps prices are stable because no one trades during

half-time. We dismiss this suggestion by tracking trading activityfor matches with a

goal just before half-time, betting interest, far from dissipating, actually ramps up over the

break. 13 Second, and as a consequence, a vigilant reader might worry that particularly heavy

half-time trading is driving our resultperhaps these markets are efficient only during thebreak. Based on our overall analysis, we strongly suspect that this is not the case, though12 Betfair Makes Online Odds on AC Milan, Hillary Clinton, Weather, Bloomberg News , September 6, 2005.Article available online at: http://quote.bloomberg.com/apps/news?pid=nifea&&sid=a8Y11XQeIcyY13 With the exception of a match between Wigan and Liverpool, in which by half time Liverpool had built upa virtually unassailable 4-0 lead. This early domination no doubt killed interest in the match odds market.


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with the data we have it is difficult to counter this formally. Certainly, we observe that these

markets are well-traded during minutes of play, even when goals just before the break excite

half-time trading. Future work may deploy statistical modelling to construct counterfactual

time-related drift, and use this to take a look at updating in-play. 14

The rest of the paper is organized as follows. The next section provides further background

on the betting industry and discusses Betfair in more detail, whilst Section III describes the

particular data set used in this study. Section IV discusses estimation strategy and presents

the main ndings. Concluding remarks are set out in Section V. Appendix A contains

supplementary materials referred to in the main text.

II. Betting and the Betfair Exchange

Traditionally, betting markets have been run by a closed community of licensed dealers,

known as bookmakers. Bookmakers are similar to market makers in nancial markets; they

establish and maintain liquid markets by quoting prices at which they will deal. In betting,

the prices are termed odds and the most common type of bet is known as a xed-oddsbet. Suppose party A wishes to back (bet on) some outcome and party B wishes to lay (bet

against) the same. Then under a xed-odds bet, A agrees to pay B a certain amount (the

backers stake) if the outcome fails to materialize, and B agrees to pay A the same stake

multiplied by pre-agreed (hence xed) odds if instead it does. For example, A might stake

$100 at odds of 3 : 1 (three to one) that Argentina will win the World Cup. In this case,

she collects $300 from B if Argentina succeed, but otherwise B keeps her $100 stake. When

betting with bookmakers, customers are restricted to backing outcomes only; the bookmakerplays the role of party B, taking the lay side to every bet.

14 Croxson and Reade (2007) lay the groundwork for research in this direction by attempting to reverseengineer goal arrival. The paper ts a bivariate poisson model to English Premiership data following theapproach laid out in Karlis and Ntzoufras (2003). Preliminary analysis suggests that probabilities impliedby Betfair prices closely track those suggested by the selected poisson process.


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Odds relate inversely to the probabilities associated with particular outcomes .15 For instance,

odds of 3 : 1 imply a view that Argentina is three times more likely to fail than to succeed (a

25% probability of Argentine victory). 16 Bookmakers rely on in-house gambling experts to

assess the likelihood of different outcomes and to compile a set of odds accordingly. As the

event draws closer the odds can be adjusted, reecting the arrival of relevant information and

the bookmakers desire to maintain a balanced book. Odds are described as fair when the

implied probabilities sum to one, but built into the set of prices offered by the bookmaker

is a return for liquidity services (known in betting circles as the overround or vigorish)

such that the sum of probabilities exceeds one. In 1999, this bookmaking model was still the

only model of betting, and bookmakers belonged to an exclusive and protable club. In the

UK, one of the worlds key betting markets, it was illegal for anyone other than a licensed

bookmaker to accept bets and a handful of major players (William Hill, Ladbrokes, Coral)

dominated the market. The overround stood at a healthy twenty two per cent .17

The arrival in 2000 of online betting exchanges marked a revolution in the industry. The

leading exchanges are essentially order-driven markets in xed-odds bets, allowing individ-

ual punters to bet with each other directly, thereby disintermediating the bookmaker. Thismeans that exchange bettors can and do lay individual outcomes, contrary to the standard

bookmaking model. In addition, exchanges allow customers to place bets in-running, once

an event is underway. This is felt to have created a signicantly more exciting betting expe-

rience. Typically, customers are charged a small commission for exchange betting services,

but the exchange does not otherwise impose any overround. Compared with bookmakers

odds, exchange prices, at least for popular events, have tended to be highly competitive. 18

15The interpretation of betting prices as probabilities is a somewhat debated area. The interested reader isreferred to Wolfers and Zitzewitz (2006) and the articles cited therein, particularly Manski (2004).16In this example, the odds are quoted in so-called fractional form. An alternative is to quote decimal odds,in which case the stake is included in the quoted multiple, so that 3 : 1 becomes 4. This is convenient becausethe implied probability is then obtained simply by inverting the decimal odds, e.g., 1 / 4 = 0.25.17 Merrill Lynch Research, 17 January 2006.18 The dominant exchange, Betfair, claims its prices are on average 20% more generous than bookmakers.Ozgit (2005) conrms the competitiveness of Betfairs pricing in the Basketball markets, although he does


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The real hurdle for exchanges has been to achieve sufficient liquidity. Betfair was one of the

rst exchanges to market, and is now by far the largest. It levies a standard commission of

5% on winning bets, falling to 2% for the heaviest users. Betfairs early entry into the mar-

ket and its decision to run with a model much closer to a standard nancial exchange than

some of its competitors (notably Flutter.com) are thought to have been pivotal its success. 19

Volumes on the exchange are estimated to have doubled from $5.23bn to $11.06bn between

2003 and 2004, and almost doubled again between 2004 and 2005 .20 These growth rates are

well ahead of those for the gambling market generally. Figure I benchmarks Betfair to the

worlds largest nancial exchanges in terms of trade frequency. 21 Betfair processes around

two million trades a daysix times the number of trades on the London Stock Exchange.

The selection of markets Betfair offers is vast and covers most sporting events of popular

interest, together with many non-sporting events (such as key political events and reality

TV). Horse racing dominates exchange turnover, followed by soccer. Within soccer betting,

customers can place bets related to the Outright Winner of a particular league or tourna-

ment, or the Top Scorer of the competition, for instance. Meanwhile, Match Odds markets

allow betting on the outcome of individual games, by backing (betting on) or laying (bettingagainst) the Home Win, Away Win, or Draw. For those with less conventional betting

draw attention to the failure of exchange sometimes to offer sufficient liquidity at inside (best) prices. Henotes that punters wishing to place large bets can be better off taking their custom back to bookmakersrather than walking down the order book at the exchange, accepting increasingly unattractive prices toget large orders lled.19 Flutter.com, founded in February 1999 by American management consultants, was the rst person-to-person betting site. Des Laffey (2005) analyzes some of the operational and marketing differences likely tohave led to Betfairs dominance over (and eventual merger with) its main rival, despite the Flutter managingto attract a comparatively huge amount of nancial backing for its launch: Flutter believed that they could

thrive by facilitating social bets between friends, for example about who would win a game of golf, andalso limited the value and frequency of bets allowed. Flutters website was not based around the Betfairidea of matching pools of money from backers and layers, instead requiring a complete match between asingle backer and a single layer. Multiple transactions on an event by a punter on Flutter were also treatedseparately which led to inefficiency whilst the Betfair model recognised mutually exclusive outcomes.20 Merrill Lynch Research, 17th January 2006.21 This gure is based on analysis undertaken by Stephen Roman, Analyst, FXCM, New York, commissionedand reported by prediction markets blog midas.org.


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Figure II. The Betfair Order Book

users) has submitted limit-orders hoping to back Arsenal asking for odds of 2.6 (i.e., slight

better than the prevailing market odds). If she were to accept $10 of this, by placing a lay

order at 2.6, the user would be betting against Arsenal and risking $26 to win $10.

III. Data

The data deployed in this paper comprise second-by-second prices and volumes from Betfairs

Match Odds markets for 185 matches played in the English Premier League during the

200607 season.23 As discussed in the previous section, Match Odds markets offer betting

on each of the possible outcomes of the event (in the case of soccer games, the Home Win,

Away Win and Draw). Our web crawler visits the order book related to bets available on

each of these outcomes and collects the following variables on a second-by-second basis:

(1) timestamp;

23 Due to technical and practical limitations it was not possible to collect betting data at high frequency forall 380 Premiership matches that took place during the season. The representativeness of the nal sampleis discussed later in this section, while Table 4 contains summary information on the matches sampled.


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(2) the game outcome to which the order book relates (e.g. Home Win);

(3) the best three back prices and the volumes available to bet at each of these prices;

(4) the best three lay prices and the volumes available to bet each of these prices;

(5) whether the market is in-play;

(6) whether the market is suspended;

(7) the total cumulative volume traded on the Match Odds market for this game.

The market for a particular match is in-play when that match is in progress. As mentioned

in the previous section, Match Odds markets for English Premiership games are heavily

traded, particularly during the games themselves. Across the sample as a whole, an averagetotal of $6.23m is traded per match. On average, 49% of the total trading volume is bet

in-running, which equates to $22,390 traded per minute and $373 per second. Many English

Premiership games are now televised (either on Sky Sports or Premiership Plus subscription

channels) and the sample features an interesting mix of televised and untelevised encounters.

Television coverage tends to boost associated Betfair trading signicantly, with non-televised

matches trading around $298 per second, and televised matches $766 .24 Summary statistics

for the 185 matches in our sample are reported in Table 4 of the appendix.

Betfair briey suspends its in-play soccer markets at kick-off and then briey again upon the

occurrence of any Material Event. 25 A Material Event in the soccer context is dened as a

goal being scored, a penalty being awarded or a player being sent off (the awarding of a red

card) .26 Goals are the most important news event and the market reaction to goals forms the

focus of the paper. Goals arrive fairly infrequently; the average number of goals per match

in our sample is 3.06, implying 0.034 goals per minute of play. During a goal-related trading

suspension Betfair discards any unlled orders, so clearing the entire order book. When the

24 The pay-per-view Premiership Plus matches, presumably because they have lower viewership, see a slightlylower volume of matched trades per second ($739) than the standard subscription Sky Sports matches ($783).25 The market suspension is on average 56 seconds long, with a median suspension of 53 seconds and standarddeviation of 20 seconds. 83% of stoppages lie within one standard deviation of the mean.26 http://content.betfair.com/aboutus/?product=exchange&region=GBR&locale=en GB&brand=betfair


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three minutes of second-half injury time. 28 Where the data suggest something extraordinary

has happened (a serious injury, or some kind of other disturbance that held up play), this

is accounted for by internet-based research to determine the nature of the incident and the

length of delay it created .29

A number of statistics can be cited to support the representativeness of our sample. No

regard to particular teams or types of matches was given when planning which matches to

sample, nonetheless our sampled matches turn out to feature a somewhat higher average

number of goals. In the entire Premiership season 2006-2007 an average of 2.45 goals was

scored per game, whereas in our sample this average is 3.06. A particular team on averagetook part in 20.9 of our sampled matches, with a standard deviation of 2.1 matches, and a

range of 9. Supporting the idea that the glamor teams were not over-selected, Middles-

brough (not a glamor side) was the team appearing most often in the sample, showing up in

26 of the sampled matches, whereas Sheffield United and Wigan Athletic were sampled more

often than Liverpool, Everton and Tottenham Hotspur, three of the bigger clubs. Star team

Manchester United appeared in only one more match than low prole Sheffield United, while

other major clubs Arsenal and Chelsea appeared in three fewer matches than Middlesbrough.Figure IV plots the average volume traded per game involving a particular team against the

frequency with which that team appears in the sample, both scaled by the corresponding

sample averages. A strong positive correlation in this plot would suggest bias towards more

heavily traded teams. Yet, despite the appearance of the big four clubs furthest right,

the correlation coefficient is just 0.29, and the regression of relative appearance frequency

on relative volume traded yields a coefficient of 0.088, with an insignicant t-value of 1.28.

It might be expected that the number of trades during a game is positively related to the28Garicano at al (2005) found that in the Spanish Primera Liga in seasons 199495 and 199899 on averagethree minutes of second-half injury time were added each game.29An example of this is the Manchester United v Blackburn Rovers match, where the effects of goals appearedlater than internet accounts reported. It transpires that, in the rst half of the match, a serious injury toone of the players had resulted in a ve minute delay to action. This was uncovered by careful reading of numerous match reports available online.


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this section, we assess the immediacy and completeness of the Betfair price response to goals

scored during the soccer games in our sample.

It is straightforward to conrm that prices respond immediately to the news of a goal. We

have preliminary evidence of this already: from plots such as those in Figure III it is apparent

that the price level has jumped between the market closing upon a goal being scored and it

reopening shortly afterwards. 30 Table I looks more closely at the magnitude of this immediate

market reaction to the four hundred and seven goals in our sample. 31

The top left cell of Table I indicates that a goal on average induces an immediate twenty

four point increase in the scoring teams probability of winning. This statistic is in line withprevious ndings on goal impact. For instance, in Gil and Levitt (2006) a World Cup 2002

goal induces a change of between twenty and thirty points in the implied probability that

the scoring team wins. Intuitively, the other cells in Table I suggest considerable variation

behind this simple average. Rows (2)(7) consider subsets of goals according to scorer type

(whether the scoring side is playing at home or away, for instance) and lateness in the game,

and columns (2)(7) subdivide goals according to the goal difference they create. Goals

that change the status quo outcome (e.g. from a draw to a win) have the greatest impact;goals that result in the scoring team leading have a greater impact (column 3) than goals

that level the scores (column 2); these goals on average immediately add respectively thirty

six and fteen percentage points to the probability that the scoring side wins. Goals that

merely boost a sides lead tend to have smaller effects. For example, a goal that extends a

teams lead to two goals (column 5) increase its win probability by just thirteen points. 32

The impact is greatest where outcome-changing goals occur towards the end of the game,

30Recall from the previous discussion that Betfair suspends the market briey in the event of a goal.31In computing this shift, we look at the market price immediately before the goal is registered and comparethis to the price shortly after the goal-induced trading suspension, once reasonable liquidity has returned tomarket.32Interestingly, Gil and Levitt (2006) report that World Cup goals which bring teams level are associatedwith shifts nearer twenty per cent, whereas goals that put a team one or two goals ahead induce shifts of around thirty percentage points. It is not inconceivable that the nature of domestic and international soccercan account for much of the discrepancy between our respective ndings on goal impact; McHale and Scarf


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Table I. Immediate goal-induced change in the implied probability that thescoring team wins.

(1) (2) (3) (4) (5) (6) (7)Goal Difference Immediately Following Goal

0 1 (+) 1 ( ) 2 (+) 2 ( ) # 3(1) All goals 0.24 0.15 0.36 0.04 0.13 0.01 0.02(2) Home goals 0.25 0.17 0.36 0.03 0.14 0.00 0.02(3) Away goals 0.24 0.14 0.37 0.04 0.18 0.00 0.02(4) Goals by favorites 0.24 0.18 0.34 0.06 0.13 0.00 0.02(5) Goals by outsiders 0.27 0.13 0.40 0.02 0.22 0.00 0.03(6) Late goals ( 80mins) 0.28 0.07 0.76 0.01 0.10 0.00 0.00(7) Late late goals ( 89mins) 0.28 0.03 0.88 0.00 0.10 N/A 0.00

as would also be expected. A goal scored very late on in the gameafter the eighty ninth

minuteand which puts a team in the lead adds a dramatic eighty eight points to the

probability that they go on to win the game (row 7, column 3).

Whereas we can be comfortable on the strength of this evidence that there is an immediate

reaction to goals in these markets, it is somewhat more complicated to ascertain whether

the jumps observed reect complete Bayesian updating or simply mark the beginning of an

updating process that takes some time to complete. To pursue this question of completeness,we might think about comparing the new post-jump price level with the level several minutes

later (assuming no further goals in that time), and construing any signicant difference

between these as evidence of informational inefficiency. For instance, Gil and Levitt (2006)

nd that Intrade soccer betting prices drift for ten to fteen minutes after goals scored in

the 2002 World Cup and interpret this as suggestive of sluggish updating. However, to

identify inefficiency in sports betting markets that are in-play one is forced to confront an

interesting and non-trivial complication: some amount of price drifting is perfectly consistentwith, and indeed evidence for, market efficiency in this setting as rational participants would

be expected continually to Bayesian update to the minor news inherent in the passage of

(2006) document important differences between these two types of soccer game, notably that there is lessdisparity in the quality of the competing teams in domestic football games.


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Figure VI. Timing of the 39 goals scored on the cusp of half-time.

40th min

41st min

42nd min

43rd min

44th min

45th min+

4

6

4

2

7

16

Timeof

goal

Numberof

goals

weeks earlier. As the rst half slips away without a goal, the probability that Chelsea wins

again begins to drift downwards. At 13:55, just after the second half commences, Chelsea

scores to move into the lead. As expected, the probability of a Chelsea win jumps upwards in

response to the goal but continues then to drift upwards over the remainder of the game. This

upward drift, at least partially, will reect rational updating to the closing window of time. It

may also reect some sluggishness in updating to the goal, and therein lies the identication

challenge. In cases such as this, we might consider modelling rational time-related price

movement as a way to identify possible drift associated with inefficiency. Modelling time-

related drift is not trivial, however; it will depend on various factors, including the current

scoreline (e.g. the magnitude of any lead) and the stage in the game. In this paper, we

introduce a simpler and arguably cleaner identication strategy; we exploit the (virtually)

newsless window provided naturally by the half-time interval. Concretely, we propose to

look at games where goals are registered on the cusp of half-time (henceforth cusp goals)

and see whether over the break in play, where time-related drift cannot be present, we detect

any news-related drift. Our data set contains thirty nine such matches.

Figure VI takes a closer look at the distribution of cusp goals by time of arrival. 33 Sixteen

of the goals occur in the nal minute of the second half (including any stoppage time), which33Six matches feature two cusp goals. We count only the time of the second goal in these cases.


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Figure VII. Tottenham Hotspur v Manchester United

(Kick off 16:00 on 02/04/2007, televised, Manchester United wins 4-0 with rst goal at 16:46)

Implied probability of Manchester United win.

0.00

0.25

0.50

0.75

1.00

16:01 16:16 16:31 16:46 17:01 17:16

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Time

US$ volume traded each minute.

0

100,000

200,000

16:01 16:16 16:31 16:46 17:01 17:16

Half Time

is encouraging, since the closer the goal to half-time, the stronger the test for semi-strong

form efficiency. A further seven goals arrive during the penultimate minute of play.

A visual look at the price series for a few of these matches is suggestive of efficient updating.

Consider, rst, Figure VII, in which Tottenham play at home to Manchester United.

This match kicks off just after 16:00 and the plot in the left hand panel shows the probabilityof a Manchester win, as implied by the best Betfair back price. This probability is 56% at

the beginning of the game (Manchester being favorites to win) but begins to drift downwards

as the rst half progresses without a goal. By the 44th minute the probability has fallen

to under 50%. Then right at the end of the rst half Manchester scores to take the lead

and the market is suspended briey. When it reopens moments later, the probability has

jumped up to 77%. Almost immediately the whistle blows for half-time. Over the fteen-

minute interval that follows, the implied probability appears to remain remarkably constantat this 77% level, suggesting that updating to the goal was immediate and complete. A

legitimate concern might be that such evidence for efficiency is an artefact of our half-time

identication strategy: perhaps prices appear not to continue to update over half-time only

because trading interest drops off during the break. In the right hand panel we report trading


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Figure VIII. Arsenal v West Ham United

(Kick-off 15:00 on 04/07/2007, not televised, West Ham United wins 1-0 with goal at 15:50)

Implied probability of Arsenal win.

0.00

0.25

0.50

0.75

1.00

15:03 15:18 15:33 15:48 16:03

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Time

US$ volume traded each minute.

0

20,000

40,000

60,000

15:03 15:18 15:33 15:48 16:03

Half Time

activity. Certainly for this game, the market is actively traded throughout half-time. In fact

trading interest appears to step up during the interval in play.

For contrast, Figure VIII illustrates the case of an upset. Here, the ex-ante favorite concedes

a goal just before half-time. The post-goal probability (again, as implied by the best Bet-

fair back price) seems somewhat more volatile but still there appears no obvious trending.

Meanwhile, trading during the half-time break is again heavy. Looking across all matches

in our sample in which a goal occurs just before half-time, betting interest always ramps

up during the interval; on average $386 is traded per second of play but $422 per second is

traded over the half-time break. 34

The apparent lack of half-time trending in such diagrams, despite heavy trading during the

break, constitutes prima facie evidence that the market is semi-strong form efficient. The

rest of this section implements regression analysis to test this formally. Pursuing a two-stepapproach, we begin by determining whether there is in fact any half-time drift, by using OLS

to estimate the following regression:

34In matches without a goal on the cusp of half time $387 per second is traded on average during the matchcompared $247 during half-time.


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pi,t = 0,i,p + 1,i,p t + i,p,t .(1)

Here, t denotes seconds during half-time; i and p index particular match (e.g. Arsenal vs.

Manchester United) and market (e.g. home win).

The average absolute trend 1,i,p over the entire sample at half-time is 2.83 10 8, and in

a quarter of the cases this gure, though tiny, is statistically signicant. 35 This slight trend

translates into a movement in probability of 1.4 10 6 per minute, and a movement in

probability of just

2.1

10

5

, or a two thousandth of a probability point, over the entirefteen minute interval.

The second step is to discern whether, to the extent that there is price drift during half-time,

this drift is connected to the arrival of the goal. For this, we estimate the following regression

model:

1,i,p = 0 + 1goal i + 22goals i + i,p ,(2)

where goal i takes 1 if goal takes place between the 40 th minute and the half-time whistle,

zero otherwise, and 2 goals i takes 1 if two goals take place in this interval, otherwise zero. 36

Additionally, because it is reasonable to suspect that pooling goals scored closest to the

interval with goals scored many minutes before the break (to which the market has had

several minutes to adjust by the time the break begins) would somewhat mask the effects of

later goals, impulse variables for each minute from the 40 th to the 45 th are created (denoted

g40i , . . . , g 45i ) and the following regression implemented:35Although some of these minuscule trends are signicant, there is no relationship between signicance andcusp goals; the tiny correlations between goal occurrence and trend signicance (there is more than onecorrelation as there is the home win, draw and away win price series, and the maximum such correlation is-.091) are just as often negative as positive.36There are six matches where two goals are scored immediately before half-time, highlighted in bold typefacein Table A.2.


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Table II. Testing the effect of cusp goals on the estimated half-time pricetrend coefficient. The number of goals in the ve minute period prior to

half time is reported rst, followed by the minute in which the cusp goal wasscored. The t -statistic is reported, with the probability of a Type I Errorgiven in parentheses below.

Number of Goals Time of Goal

1,i,p 1 2 F 40 41 42 43 44 45 FH 0.95( .35)

0.80( .42)

0.57( .57)

1.03( .30)

0.46( .65)

0.58( .57)

1.09( .28)

1.86(0 .06)

0.19( .85)

1.06( .39)

D 1.36( .17)

0.58( .57)

0.94( .39)

1.24( .22)

0.16( .87)

1.14( .25)

0.12( .91)

1.21( .23)

0.05( .96)

0.68( .67)

A 0.35( .72)

0.87( .38)

0.63( .53)

0.99( .33)

0.94( .35)

0.68( .50)

0.01(1 .00)

0.44( .66)

0.87( .39)

0.55( .77)

1,i,p = 0 + 1g40i + 2g41i + 3g42i + 4g43i + 5g44i + 6g45i + i,p .(3)

These two regression models are also run on the residual variance from ( 1), 2i,p = T 1 T

t =1 i,p,t

in order to ascertain whether pre-half-time goals impact market volatility. Table II displays

the t - and F - statistics (with the probability of a type I error in parentheses) relating to

the trend regressions ( 2) and ( 3), while Table III reports the same output for the variance

regressions.

From Table II all the impulse variables in regression models ( 2) and (3) are insignicant;

goals in the ve minutes before half-time have no impact on the drift observed during the

half-time interval. Goals in the 44 th minute are close to exerting a signicant effect, but

jointly all such goal variables are highly insignicant from the F -test statistic. No other

individual variable for any type of goal is close to signicant.

From Table III it appears that cusp goals can have an impact on the volatility of the priceof a home team win; although the effect of one cusp goal is insignicant, two such goals

has a considerable impact, with a t -statistic of 2.45 .37 Considering just individual goals,

37As the F -test, to a crude approximation, is the square of the t -test, the joint signicance displayed in thesignicant F -test statistic can mainly be attributed to the two goals effect.


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Table III. Testing the effect of cusp goals on the estimated variance of half-time prices. The number of goals in the ve minute period prior to half time

is reported rst, followed by the minute in which the goal was scored. Thet -statistic is reported, where a value above 2 denotes statistical signicance.

Number of Goals Time of Goal

2i,p 1 2 F 40 41 42 43 44 45 FH 1.10( .27)

2.45( .02)2.45( .02)2.45( .02)

5.21( .01)5.21( .01)5.21( .01)

1.63( .10)

0.03( .98)

0.14( .89)

0.01( .99)

2.12( .04)2.12( .04)2.12( .04)

1.61( .11)

1.52( .17)

D 0.22( .82)

0.16( .87)

0.06( .94)

0.29( .77)

0.49( .63)

0.32( .75)

0.25( .80)

0.96( .34)

0.36( .72)

0.26( .95)

A 0.17( .86)

0.22( .83)

0.03( .97)

0.19( .85)

1.05( .29)

0.18( .86)

0.15( .89)

0.19( .85)

0.29( .77)

0.23( .97)

44th

minute goals are individually signicant, with at-statistic of 2.12, although the joint

F -statistic of all ve impulse variables remains insignicant. It appears, however, that the

volatility impact of cusp goals is limited to the home-win market, as no other variables are

signicant in Table III .

We do not interpret these selected signicant coefficients for volatility as evidence of market

inefficiency for a number of reasons. First, many statistical tests are being carried out, and

when taking a 5% signicance level, one expects spurious rejections around 5% of the time;

of forty eight t -tests, two fail, which is under 5%. Further, the effect is a variance effectand not a trend effect, and so not evidence for post-news trending. Also, the volatility effect

appears to be strong only where two cusp goals occur in a single game, an uncommon event.

As such we take the broad sweep of the evidence presented in Tables II and Table III as

providing support for informational efficiency: these soccer markets rapidly and completely

impound the news of a goal.

V. Concluding Remarks

The recent emergence of online betting exchanges has made it possible to obtain high-

frequency data relating to bets placed in-running (during a live sports event). This implies

a fertile new setting for empirical work, and in particular, it paves the way for a cleaner look


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25

at the topic of market efficiency. A market that is semi-strong form efficient updates swiftly

and fully to publicly available information. A problem for those seeking to put this to

the test in nancial markets has been the possibility of news leakage not observed by the

econometrician. In sports, however, major news (such as a goal in soccer) tends to break

comparatively cleanly. We exploit this characteristic of sports events to offer a fresh study

of efficiency. Prices for soccer-related markets are extracted from the live order book of the

largest online betting exchange, Betfair.com, and tested for efficiency in relation to the arrival

of goals. A complication particular to this exercise relates to the hypothetical difficulty in

determining whether any price drift following a goal should be interpreted as sluggishness in

updating (and hence evidence of inefficiency), or be considered simply an efficient response to

the passage of time without a goal (goalless periods of play being themselves price-relevant

news). To overcome this identication issue, we exploit the naturally newsless half-time

intervalwe study matches where goals arrive on the cusp of the half-time break. Our

ndings suggest that prices rapidly and completely impound news.

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[11] Fama, E.F., The Behaviour of Stock Market Prices, The Journal of Business , XXXVIII (1965), 34105.

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[19] Kothari, S.P. and J.B. Warner, Measuring long-horizon security price performance, Journal of Finan-cial Economics XLIII (1997), 301339.[20] Laffey, D., Against the OddsCase Study of Betfair.com, The International Journal of Entrepreneur-

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Appendix A. Additional Tables

Table A.1. Summary Statistics for 200607 English Premiership Games. H(A) is home (away) team goals scored, % in play is the share of the total amounttraded in-running. In the TV column, N denotes an untelevised match, SSrepresents a match televised on Sky Sports, and PP a match televised onPremiership Plus. Upset reects whether the match outcome was expected(Y) or not (N), with (D) representing a draw, and (n/a) indicating that nodata were available on pre-match odds. Finally the H-T? column is Y if half-time data exist for the efficiency testing exercise in Section 4.

Goals TradedDate Home Away Time TV H A Upset $m In-play H-T?

8/19/06 Arsenal Aston Villa 15:00 N 1 1 D 4.84 49% Y8/19/06 Bolton Tottenham 17:15 PP 2 0 N 4.49 64% Y8/19/06 Sheff Utd Liverpool 12:45 SS 1 1 D 9.97 48% Y8/20/06 Chelsea Man City 16:00 SS 3 0 N 7.95 29% Y8/20/06 Man Utd Fulham 13:30 PP 5 1 n/a 3.98 23% N8/22/06 Tottenham Sheff Utd 20:00 N 2 0 n/a 2.82 24% Y8/22/06 Watford West Ham 19:45 N 1 1 D 1.38 35% Y8/23/06 Charlton Man Utd 20:00 SS 0 3 N 6.75 45% Y8/23/06 Middlesbrough Chelsea 20:00 N 2 1 n/a 3.82 42% Y8/26/06 Charlton Bolton 15:00 N 2 0 n/a 0.93 27% Y8/26/06 Fulham Sheff Utd 15:00 N 1 0 n/a 0.80 26% Y8/26/06 Liverpool West Ham 12:45 PP 2 1 N 7.06 69% Y8/26/06 Man City Arsenal 17:15 PP 1 0 Y 10.33 57% Y8/26/06 Tottenham Everton 15:00 N 0 2 Y 1.33 49% Y8/26/06 Watford Man Utd 15:00 N 1 2 n/a 6.69 45% Y8/27/06 Aston Villa Newcastle 14:00 N 2 0 N 3.15 60% Y8/27/06 Blackburn Chelsea 16:00 SS 0 2 N 11.54 47% Y8/28/06 Middlesbrough Portsmouth 20:00 SS 0 4 Y 8.42 43% Y9/16/06 Bolton Middlesbrough 15:00 N 0 0 D 2.26 34% Y9/16/06 Charlton Portsmouth 12:45 PP 0 1 Y 4.54 66% Y9/16/06 Everton Wigan 15:00 N 2 2 n/a 3.77 37% Y9/16/06 Sheff Utd Reading 15:00 N 1 2 N 1.01 55% N9/16/06 Watford Aston Villa 17:15 PP 0 0 n/a 6.29 58% Y9/17/06 Blackburn Man City 15:00 N 4 2 N 1.06 19% Y9/17/06 Chelsea Liverpool 13:30 SS 1 0 N 11.63 55% Y9/17/06 Man Utd Arsenal 16:00 SS 0 1 Y 14.38 43% N9/17/06 Tottenham Fulham 15:00 N 0 0 D 2.01 27% Y9/17/06 West Ham Newcastle 15:00 N 0 2 n/a 1.18 35% Y9/20/06 Liverpool Newcastle 20:00 N 2 0 N 4.26 10% N9/23/06 Arsenal Sheff Utd 15:00 N 3 0 N 5.79 26% Y9/23/06 Aston Villa Charlton 15:00 N 2 0 N 1.02 29% Y9/23/06 Fulham Chelsea 15:00 N 0 2 N 4.26 53% Y9/23/06 Man City West Ham 15:00 N 2 0 N 1.02 24% Y

continued on next page.. .


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. . . continued from previous page Goals Traded

Date Home Away Time TV H A Upset $m In-play H-T?9/23/06 Middlesbrough Blackburn 15:00 N 0 1 Y 0.56 28% Y9/23/06 Wigan Watford 15:00 N 1 1 D 0.69 18% Y9/24/06 Newcastle Everton 16:00 SS 1 1 D 7.34 67% Y9/30/06 Charlton Arsenal 15:00 N 1 2 N 4.82 50% Y9/30/06 Chelsea Aston Villa 15:00 N 1 1 D 6.70 48% Y9/30/06 Everton Man City 15:00 N 1 1 n/a 2.36 16% Y9/30/06 Sheff Utd Middlesbrough 17:15 PP 2 1 Y 5.39 72% Y

10/14/06 Reading Chelsea 17:15 PP 0 1 N 11.47 62% Y10/22/06 Blackburn Bolton 15:00 N 0 1 Y 1.08 59% Y10/22/06 Middlesbrough Newcastle 15:00 N 1 0 N 1.18 56% Y10/22/06 Reading Arsenal 16:00 SS 0 4 N 7.71 47% Y10/22/06 Tottenham West Ham 15:00 N 1 0 N 1.84 41% Y11/4/06 Bolton Wigan 15:00 N 0 1 n/a 1.86 55% N11/4/06 Fulham Everton 12:45 SS 1 0 Y 5.00 68% Y11/4/06 Liverpool Reading 15:00 N 2 0 n/a 3.68 32% Y11/4/06 Man Utd Portsmouth 15:00 N 3 0 N 4.47 37% Y11/4/06 Newcastle Sheff Utd 17:15 PP 0 1 Y 7.50 61% Y11/4/06 Watford Middlesbrough 15:00 N 2 0 N 0.80 35% Y11/5/06 Aston Villa Blackburn 14:00 N 2 0 N 2.37 28% Y11/5/06 Tottenham Chelsea 16:00 SS 2 1 Y 16.34 54% Y

11/11/06 Blackburn Man Utd 17:15 PP 0 1 N 9.97 64% Y11/11/06 Chelsea Watford 15:00 N 4 0 N 7.80 18% Y11/11/06 Everton Aston Villa 15:00 N 0 1 Y 1.40 56% Y11/11/06 Man City Newcastle 12:45 SS 0 0 D 5.46 61% Y11/11/06 Middlesbrough West Ham 15:00 N 1 0 N 0.81 50% Y11/11/06 Portsmouth Fulham 15:00 N 1 1 D 1.10 44% Y11/11/06 Sheff Utd Bolton 15:00 N 2 2 D 0.66 47% Y11/11/06 Wigan Charlton 15:00 N 3 2 n/a 1.15 27% Y11/12/06 Arsenal Liverpool 16:00 SS 3 0 N 12.96 50% Y11/18/06 Middlesbrough Liverpool 17:15 PP 0 0 D 8.65 47% Y11/19/06 Blackburn Tottenham 16:00 SS 1 1 D 9.61 64% Y11/19/06 Wigan Aston Villa 13:30 SS 0 0 D 5.72 50% Y11/25/06 Aston Villa Middlesbrough 15:00 N 1 1 n/a 2.02 40% Y11/25/06 Bolton Arsenal 17:15 PP 3 1 Y 10.56 64% Y11/25/06 Fulham Reading 15:00 N 0 1 Y 1.48 35% Y11/25/06 Liverpool Man City 15:00 N 1 0 N 6.50 58% Y11/25/06 West Ham Sheff Utd 15:00 N 1 0 N 2.04 23% Y11/26/06 Man Utd Chelsea 16:00 SS 1 1 D 15.38 61% Y11/26/06 Newcastle Portsmouth 13:30 SS 1 0 N 6.38 66% Y11/26/06 Tottenham Wigan 15:00 N 3 1 N 2.05 52% Y12/2/06 Blackburn Fulham 15:00 N 2 0 N 2.58 19% Y12/2/06 Middlesbrough Man Utd 17:15 PP 1 2 N 12.09 59% Y12/2/06 Portsmouth Aston Villa 15:00 N 2 2 D 2.58 54% Y12/2/06 Reading Bolton 15:00 N 1 0 N 0.85 63% Y12/2/06 Sheff Utd Charlton 15:00 N 2 1 N 1.95 35% Y12/2/06 Wigan Liverpool 15:00 N 0 4 N 2.76 37% Y



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Date Home Away Time TV H A Upset $m In-play H-T?12/3/06 Everton West Ham 16:00 SS 2 0 N 9.42 62% Y

12/13/06 Chelsea Newcastle 19:45 N 1 0 N 16.21 37% Y12/13/06 Wigan Arsenal 19:45 SS 0 1 N 11.36 42% Y12/17/06 West Ham Man Utd 16:00 SS 1 0 Y 13.99 53% Y12/18/06 Fulham Middlesbrough 20:00 SS 2 1 N 10.25 52% Y12/23/06 Arsenal Blackburn 15:00 N 6 2 n/a 2.37 53% N12/23/06 Aston Villa Man Utd 15:00 N 0 3 N 4.27 52% N12/23/06 Fulham West Ham 12:45 SS 0 0 D 3.47 47% Y12/23/06 Liverpool Watford 15:00 N 2 0 n/a 3.99 32% N12/23/06 Middlesbrough Charlton 15:00 N 2 0 N 1.58 17% N12/23/06 Newcastle Tottenham 15:00 N 3 1 n/a 0.85 69% N12/23/06 Reading Everton 15:00 N 0 2 Y 1.18 30% N12/26/06 Blackburn Liverpool 15:00 SS 1 0 Y 3.74 71% Y12/26/06 Bolton Newcastle 15:00 N 2 1 N 2.26 26% Y12/26/06 Everton Middlesbrough 15:00 N 0 0 n/a 1.03 37% Y12/26/06 Man Utd Wigan 15:00 N 3 1 n/a 6.95 37% Y12/26/06 Sheff Utd Man City 15:00 N 0 1 Y 0.56 49% Y12/26/06 Watford Arsenal 17:30 SS 1 2 N 10.89 36% Y12/27/06 Charlton Fulham 20:00 SS 2 2 D 9.97 54% N12/30/06 Blackburn Middlesbrough 15:00 N 2 1 N 1.18 35% Y12/30/06 Bolton Portsmouth 15:00 N 3 2 N 2.02 27% Y12/30/06 Chelsea Fulham 15:00 N 2 2 D 10.39 42% Y12/30/06 Everton Newcastle 15:00 N 3 0 N 0.96 35% Y1/13/07 Blackburn Arsenal 17:15 PP 0 2 N 9.94 91% Y1/13/07 Bolton Man City 15:00 N 0 0 n/a 2.59 31% Y1/13/07 Charlton Middlesbrough 15:00 N 1 3 Y 0.76 48% Y1/13/07 Chelsea Wigan 15:00 N 4 0 N 6.50 24% Y1/13/07 Man Utd Aston Villa 15:00 N 3 1 n/a 3.79 41% Y1/13/07 Watford Liverpool 12:45 PP 0 3 N 6.85 40% Y1/13/07 West Ham Fulham 15:00 N 3 3 D 1.97 41% Y1/14/07 Everton Reading 13:45 SS 1 1 D 6.59 49% Y1/14/07 Tottenham Newcastle 16:00 SS 2 3 Y 9.17 74% Y1/20/07 Aston Villa Watford 15:00 N 2 0 N 2.32 22% Y1/20/07 Fulham Tottenham 15:00 N 1 1 D 3.46 21% Y1/20/07 Man City Blackburn 17:15 PP 0 3 Y 6.26 72% Y1/20/07 Middlesbrough Bolton 15:00 N 5 1 n/a 1.34 53% Y1/20/07 Newcastle West Ham 15:00 N 2 2 D 2.54 46% Y1/20/07 Portsmouth Charlton 15:00 N 0 1 Y 2.12 37% Y1/20/07 Reading Sheff Utd 15:00 N 3 1 N 1.07 29% Y1/21/07 Arsenal Man Utd SS 2 1 N 12.89 65% Y1/21/07 Wigan Everton 12:45 PP 0 2 Y 3.33 66% Y1/30/07 Portsmouth Middlesbrough N 0 0 D 1.89 36% Y1/30/07 Reading Wigan 20:00 N 3 2 N 3.65 29% Y1/30/07 Sheff Utd Fulham N 2 0 N 2.20 38% Y1/30/07 West Ham Liverpool 19:45 SS 2 1 Y 13.16 57% Y2/3/07 Aston Villa West Ham 15:00 N 1 0 N 2.40 24% Y



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Date Home Away Time TV H A Upset $m In-play H-T?2/3/07 Blackburn Sheff Utd 15:00 N 2 1 N 0.95 18% Y2/3/07 Charlton Chelsea 15:00 N 0 1 n/a 6.39 49% Y2/3/07 Fulham Newcastle 15:00 N 2 1 N 0.96 30% Y2/3/07 Liverpool Everton 12:45 PP 0 0 D 10.11 57% Y2/3/07 Man City Reading 15:00 N 0 2 Y 1.04 26% Y2/3/07 Middlesbrough Arsenal 17:15 PP 1 1 D 8.92 68% Y2/3/07 Watford Bolton 15:00 N 0 1 N 0.72 22% Y2/3/07 Wigan Portsmouth 15:00 N 1 0 N 0.48 26% Y2/4/07 Tottenham Man Utd 16:00 SS 0 4 N 8.98 46% Y

2/10/07 Chelsea Middlesbrough 15:00 N 3 0 N 3.37 45% Y2/10/07 Everton Blackburn 15:00 N 1 0 N 0.74 19% Y2/10/07 Man Utd Charlton 15:00 N 2 0 N 5.61 41% Y2/10/07 Newcastle Liverpool 15:00 N 2 1 Y 4.10 37% Y2/10/07 Portsmouth Man City 17:15 PP 2 1 N 9.19 61% Y2/10/07 Reading Aston Villa 12:45 PP 2 0 N 4.70 61% Y2/10/07 Sheff Utd Tottenham 15:00 N 2 1 Y 0.84 39% Y2/10/07 West Ham Watford 15:00 N 0 1 Y 3.00 23% Y2/11/07 Arsenal Wigan 16:00 SS 2 1 N 14.21 34% Y2/21/07 Everton Tottenham 20:00 N 1 2 Y 1.34 61% Y2/21/07 Watford Wigan 19:45 N 1 1 D 0.67 53% Y2/24/07 Charlton West Ham 15:00 N 4 0 N 1.35 65% Y2/24/07 Fulham Man Utd 12:45 SS 1 2 N 11.22 64% Y2/24/07 Liverpool Sheff Utd 15:00 N 4 0 N 4.97 32% Y2/24/07 Middlesbrough Reading 15:00 N 2 1 N 1.10 51% Y2/24/07 Watford Everton 17:15 PP 0 3 N 5.17 67% Y3/3/07 Liverpool Man Utd 12:45 SS 0 1 Y 9.12 55% N3/3/07 Arsenal Reading 15:00 N 2 1 Y 5.99 58% N3/3/07 Fulham Aston Villa 15:00 N 1 1 n/a 0.81 34% Y3/3/07 Man City Wigan 15:00 N 0 1 n/a 0.80 46% Y3/3/07 Newcastle Middlesbrough 15:00 N 0 0 n/a 0.95 45% Y3/3/07 Sheff Utd Everton 15:00 N 1 1 n/a 0.54 40% Y3/3/07 Wa

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