# Jan Vecer Tomoyuki Ichiba Mladen Laudanovic. Harvard University, September 29, 2007

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1 Sports Sports Tomoyuki Ichiba Mladen Laudanovic Department of Statistics, Columbia University, vecer Harvard University, September 29, 2007

2 Abstract Sports In this talk we introduce a quantitative measure of the excitement of sports games. This measure can be thought of as the variability of the expectancy of winning as a game progresses. We illustrate the concept of excitement at soccer games for which the theoretical win expectancy can be well approximated from a Poisson model of scoring. We show that in the Poisson model, higher scoring rates lead to increased expected excitement. Given a particular strength of a team, the most exciting games are expected with opponents who are slightly stronger. We apply this theory to the FIFA 2006 games, where the winning expectancy was independently estimated by betting markets. Thus it was possible to compute the expected and the realized excitement of each given game from the trading data.

3 Sports Higher Excitement comes with More changes in the score lead. Changes that are more dramatic. Decisive moments later in the game. Fluctuation of satisfies the above criterions. More changes to the, the less predictable is the outcome of the game.

4 Sports We propose the following measure for excitement: Excitement = Variability of the ning. (1) Variability can be measured as the Total Variation (TV): TV (f ) = lim f (ti+1 ) f (t i ), (2) max t i+1 t i 0 where 0 = t 0 < t 1 < < t n = T is a partition of the interval [0, T ]. Total variation can be viewed as the vertical component of the arc-length of the graph of a given function f. The longer the path of win expectancy for a given team, the more swings there are in the game, and thus the game is more exciting.

5 - Continued Sports Formally, we can define Excitement = TV(Probability of Team 1 s) + TV(Probability of Team 2 s). (3)

6 - Continued Sports This definition makes sense if a draw is not an option, such as in the elimination round games. If a draw is possible, we can use a modified version: Excitement = TV(Probability that Team 1 s) + TV(Probability of Draw) + TV(Probability that Team 2 s). (4)

7 Explained Sports In general, the total variation of winning expectancy is mostly changed if there is a game deciding event close to the end of the game, if there are a number of events where the game lead is changed, or if the weaker team unexpectedly wins or draws the game. On the other hand, only small changes in the total variation of winning probabilities occur when the game is one sided, with an early lead from the favorite team.

8 Sports Our approach to quantitatively measure the excitement via variability of the win expectancy described in this talk is novel and it could be applied to all sports played with two competing teams. can be computed/observed from The Model. Betting Market.

9 Measurement from Model Sports There are already some cases of sports specific attempts to measure excitement based on model. The problem is that for many sports, it is not entirely clear how to determine the win expectancy during the course of the game. The evolution of the game can be modeled with rather simplifying assumptions for only a small number of sports. The simplest and analytically tractable models assume no memory during the game which suggests the use of Markov models. Sports whose evolution could be approximated well by Markov models include baseball, tennis, soccer, or hockey.

10 from Betting Market. Sports Betting market (in play) provides excellent feedback on measuring probability of win/loss of a certain team. Betting markets are now organized as major financial exchanges (such as betfair.com), and thus are very efficient and reliable in estimating the correct at any given point during the game. US entities cannot participate in the market for legal reasons, but the betting exchange trades US sports events as well (traded by foreign entities).

11 Focus of the Talk Sports We will focus on soccer in this talk since the it is both tractable from the model, and there is enough data from the betting market.

12 Sports Poisson process is a memoryless counting process (i.e., taking integer values) in continuous time. Its evolution is given by P(X t+ t = X t X t ) = 1 λ( t) + o( t), (5) P(X t+ t = X t + 1 X t ) = λ( t) + o( t). (6)

13 Score Evolution Sports We assume that the scores of the two teams evolve as independent Poisson processes. In particular, if X T : Y T denotes the final score of the game, we have and P(X T = X t + k) = exp[ λ(t t)] P(Y T = Y t + k) = exp[ µ(t t)] [λ(t t)]k, k! [µ(t t)]k, k! where X t : Y t is the current score at time t in the game. Variable T is the end time of the game, and it is assumed to be fixed.

14 Score Evolution Sports Parameters λ and µ are called the scoring intensities of the two teams. They are related to the expected score by the following relationship: E[X T X t X t ] = λ(t t), E[Y T Y t Y t ] = µ(t t). Thus one should expect to see on average λ(t t) and µ(t t) goals for the two teams in the remaining T t time of the game.

15 Sports The Poisson model of scoring could be used for modeling the evolution of scoring in soccer or hockey games. The memoryless property implies that the time between the goals is exponentially distributed with parameters λ and µ respectively. In this model we can get explicit formulas for the win expectancy of each team, and the expectancy of a draw.

16 - Continued Sports If the current time is t [0, T ], and the current score is X t : Y t, we have P(Team 1 Will ) = P(X T > Y T ) = = P(X T = k X t, Y T < k X t ) k=0 = [ k=0 e λ t λk t k! k+x t Y t 1 i=0 e µ t µi t i! ] (7)

17 - Continued Sports P(Draw) = P(X T = Y T ) = [ = e (λt+µt) k=0 λ (k+max(x t +Yt ) Xt ) t (k+max(x t +Y t ) X t )! ] µ (k+max(x t +Yt ) Yt ) t (k+max(x t +Y t ) Y t )! (8)

18 - Continued Sports P(Team 2 Will ) = P(Y T > X T ) = = P(Y T = k Y t, X T < k Y t ) k=0 = [ k=0 e µt µk t k! k X t +Y t 1 i=0 e λt λi t i! ] (9)

19 Holland - Argentina Sports Evolution of probabilities (implied by a Poisson model of scoring) of a draw, win of Holland and win of Argentina during the Holland - Argentina game. The game ended with a 0:0 draw. The scoring intensity for Holland was estimated at 1.05, and for Argentina at 1.57.

20 Draw Sports 1 P Draw Time Minutes

21 Holland Sports 1 P Holland Time Minutes

22 Argentina Sports P Argentina Time Minutes

23 Togo - France Sports Evolution of probabilities (implied by a Poisson model of scoring) of a draw, win of Togo and win of France during the Togo - France game. France scored in the 55th and the 61st minute of the match. The betting market estimated the scoring intensities to be 0.37 for Togo and 2.65 for France. The expected excitement was 1.28, the lowest for all the games played in that championship.

24 Draw Sports 1 P Draw Time Minutes

25 Togo Sports 1 P Togo Time Minutes

26 France Sports 1 P France Time Minutes

27 Ghana - USA Sports Evolution of probabilities (implied by a Poisson model of scoring) of a draw, win of Ghana and win of United States during the Ghana - United States game. Ghana scored in the 22nd minute, followed by the goal of United States in the 43rd minute and the second goal of Ghana in the 47th minute. The scoring intensity for Ghana was estimated at 1.37, and for the United States at The expected excitement was 2.55, the highest expectation among all the games in the championship.

28 Draw Sports 1 P Draw Time Minutes

29 Ghana Sports 1 P Ghana Time Minutes

30 USA Sports 1 P USA Time Minutes

31 Sports Game λ 1 λ 2 Score Loss Draw Total Model Exp Spain France Italy Australia Germany * Argentina Argentina * Mexico Italy * France Portugal Holland Brazil France England Portugal * Germany Portugal England Ecuador Portugal France Switzerland Ukraine * Germany Italy * Italy Ukraine Germany Sweden Brazil Ghana Table: Elimination round games ordered by the excitement level, regulation time plus injury time. Draw was a possible outcome. marked by a star ended up with draw, and went into overtime.

32 Sports Game Score T1 Advance T2 Advance Total England Portugal * Portugal Holland Italy * France Switzerland Ukraine * Germany * Argentina Spain France Italy Australia Brazil France Argentina Mexico Germany Italy Germany Portugal Portugal France England Ecuador Italy Ukraine Germany Sweden Brazil Ghana Table: Elimination round games ordered by the excitement level, including overtime. Draw was not an option. marked by a star went into penalty shootouts.

33 Expected Excitement - Sports Λ Μ Figure: Expected excitement as a function of intensities of scoring.

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