Modelling the distribution of first innings runs in T20 Cricket

Transcription

1 Modelling the distribution of first innings runs in T20 Cricket James Kirkby The joy of smoothing James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 1 / 22

2 Introduction Cricket for the uninitiated Figure : Muralitharan to Gilchrist James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 2 / 22

3 Introduction Motivation Why we might we interested in cricket data? James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 3 / 22

4 Introduction Motivation Why we might we interested in cricket data? Because we love cricket? James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 3 / 22

5 Introduction Motivation Why we might we interested in cricket data? Because we love cricket? Well some of us do James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 3 / 22

6 Introduction Motivation Why we might we interested in cricket data? Because we love cricket? Well some of us do Because it s not the Iris or the Old Faithful data James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 3 / 22

7 Introduction Motivation Why we might we interested in cricket data? Because we love cricket? Well some of us do Because it s not the Iris or the Old Faithful data There is lots of cricket data Discrete nature of the game, means that large quantities of data are available Statistics are already an important aspect of the game James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 3 / 22

8 Introduction Motivation Why we might we interested in cricket data? Because we love cricket? Well some of us do Because it s not the Iris or the Old Faithful data There is lots of cricket data Discrete nature of the game, means that large quantities of data are available Statistics are already an important aspect of the game Gambling James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 3 / 22

9 Introduction Motivation Why we might we interested in cricket data? Because we love cricket? Well some of us do Because it s not the Iris or the Old Faithful data There is lots of cricket data Discrete nature of the game, means that large quantities of data are available Statistics are already an important aspect of the game Gambling Standing on the shoulders of giants Working out the odds of dice and card games is what inspired the first interest in statistics and probability James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 3 / 22

10 Data Scope of the Data There are a vast number of matches played worldwide each year for which data is publicly available We are going to restrict attention to the following types of matches: T20 cricket, ie 20 overs per team Only Top Tier competitions: T20 internationals, English County T20s, IPL, Big Bash, South African T20 James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 4 / 22

11 Data Scope of the Data There are a vast number of matches played worldwide each year for which data is publicly available We are going to restrict attention to the following types of matches: T20 cricket, ie 20 overs per team Only Top Tier competitions: T20 internationals, English County T20s, IPL, Big Bash, South African T20 We are going to be modelling the number runs teams score in an innings, and so we First Innings (only data for the team that bats first) Full allocation of overs was available, ie not weather affected James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 4 / 22

12 Data Scope of the Data There are a vast number of matches played worldwide each year for which data is publicly available We are going to restrict attention to the following types of matches: T20 cricket, ie 20 overs per team Only Top Tier competitions: T20 internationals, English County T20s, IPL, Big Bash, South African T20 We are going to be modelling the number runs teams score in an innings, and so we First Innings (only data for the team that bats first) Full allocation of overs was available, ie not weather affected These restrictions lead to a sample of 1138 matches James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 4 / 22

13 Data Data Description We observe the progression of runs that a team scores through the innings At the beginning of each over we have the following information: The number of runs scored in the remainder of the innings The number of wickets down / number of batsmen remaining The number of overs / balls remaining James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 5 / 22

14 Data Data Description We observe the progression of runs that a team scores through the innings At the beginning of each over we have the following information: The number of runs scored in the remainder of the innings The number of wickets down / number of batsmen remaining The number of overs / balls remaining We will focus on the run rate (runs per over) to ensure that results are comparable with different numbers of overs remaining James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 5 / 22

15 Data Data Description We observe the progression of runs that a team scores through the innings At the beginning of each over we have the following information: The number of runs scored in the remainder of the innings The number of wickets down / number of batsmen remaining The number of overs / balls remaining We will focus on the run rate (runs per over) to ensure that results are comparable with different numbers of overs remaining Definition We define the random variable, Y W,R as the subsequent run rate a team achieves given that they are currently W wickets down with R overs remaining in the innings James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 5 / 22

16 Data Our Aim We would like to estimate the distributions of the various Y W,R with the following requirements Avoid a full rank method - don t want be storing the entire data set in order to evaluate probabilities Want to be able to easily evaluate the probabilities from the distribution We would like a set of consistent distributions ie the probability of achieving any given run rate should be lower if a team has fewer wickets remaining James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 6 / 22

17 Data Observed Data Frequency James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 7 / 22

18 Data Empirical Distribution James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 8 / 22

19 Data Empirical Distribution James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 9 / 22

20 Model Notation We observe many realisations of each of the Y W,R We will refer to the i th realisation of Y W,R, when W = w and R = r, as y w,r,i When it is clear from the context which W and R we are talking about, or if it doesn t matter, we will drop the subscripts and use Y and y i James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 10 / 22

21 Model Distribution Assumption We assume that Y follows a spline distribution, with pdf given by: m f(y) = B j(y)α j (1) j=1 Sufficient conditions for a valid pdf are: α j > 0 and m α j = 1 (2) j=1 We can remove the need for the first condition by re-parameterizing to: m f(y) = B j(y) exp(a j) (3) j=1 James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 11 / 22

22 Model Likelihood The log-likelihood for our data given the spline distribution l(a; y) = 1 T n log (B exp(a)) (4) where B = b 1(y i) b m(y i) b 1(y n) b m(y n) and a = a 1 a m (5) James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 12 / 22

23 Model Estimation Estimation of the parameters can now proceed by finding the roots of the Lagrangian: L(a, γ) = 1 T log (B exp a) + γ ( 1 T m exp a 1 ) (6) James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 13 / 22

24 Model Estimation Estimation of the parameters can now proceed by finding the roots of the Lagrangian: L(a, γ) = 1 T log (B exp a) + γ ( 1 T m exp a 1 ) (6) The gradient vectors are: ( ) T ( ) T L a = 1 1 (B diag(exp a)) + γ exp a = (B diag(α)) + γα (7) B exp a Bα and L γ = ( 1 T m exp a 1 ) = ( 1 T mα 1 ) (8) James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 13 / 22

25 Model Estimation The hessian of the our objective function is [ ( diag L H a,γl = γ exp a) ] V T U 1 V exp a a, (9) (exp a) T 0 where U = diag (B exp a) 2 and V = B diag(exp a) This can be combined with expressions (7) and (8) to find the maximum likelihood estimate of the coefficients, a, using Newton-Raphson James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 14 / 22

26 Model Result James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 15 / 22

27 Model Further Smoothing We would like to impose some smoothness on the distributions, so that when the number of wickets remaining and overs remaining is similar we have a similar distribution We can achieve this by imposing a difference penalty on the parameters of the neighbouring distributions In order to be able to add the penalty we first need to be able to estimate the parameters jointly, which requires that we make a couple of tweaks to our basis and likelihood James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 16 / 22

28 Model Multi-density Basis In order to model the distributions joint, you would naively define the basis as: B W =0,R= B W =1,R= B W =2,R= B = B W =8,R= B W =9,R=1 James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 17 / 22

29 Model Multi-density Basis In order to model the distributions joint, you would naively define the basis as: B W =0,R= B W =1,R= B W =2,R= B = B W =8,R= B W =9,R=1 This part of the basis does not support any data! James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 17 / 22

30 Model Multi-density Basis So after removing columns from the basis which support no observations, we have something like: B W =0,R= B W =0,R= B W =1,R= B = B W =8,R= B W =9,R=1 James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 18 / 22

31 Model Multi-density Basis We also need to define a summing matrix to enforce the constraints in the Lagrangian : 1 m m m 0 0 N = m m Clearly we will need to define an analogue of B for N, which we will refer to as Ñ James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 19 / 22

32 Model Bring on the smoothing Our unpenalised target function becomes ( ) ) L(ã, γ) = 1 T log B exp ã + γ (Ñ T exp ã 1 (10) We can then simply add add a difference penalty to impose smoothness across our distributions: L P(ã, γ) = L(ã, γ) λ exp(ã) T DT D exp(ã), (11) where D is matrix that has been chopped down from some difference matrix D For our example, we will use D = D W D R I m James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 20 / 22

33 Model Result James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 21 / 22

34 Model Further Work Would be good to take account of the repeated measurements in the data James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 22 / 22

35 Model Further Work Would be good to take account of the repeated measurements in the data Find a way to introduce a parametric component into the model James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 22 / 22

36 Model Further Work Would be good to take account of the repeated measurements in the data Find a way to introduce a parametric component into the model Performance improvements - Woodbury Matrix Identity / Schur Complement James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 22 / 22

37 Model Further Work Would be good to take account of the repeated measurements in the data Find a way to introduce a parametric component into the model Performance improvements - Woodbury Matrix Identity / Schur Complement Alternative penalty structure - add a penalty to ensure the CDFs do not cross James Kirkby Modelling the distribution of first innings runs in T20 Cricket The joy of smoothing 22 / 22