Yes, we all know the Kolkata Knight Riders (KKR) won the Indian Premier League 2014, but who among all the teams’ many players really did well? And were they awarded for it? The former is a decidedly subjective question based on what you consider goodness in a cricketer – especially one playing in a new format of the game, Twenty20, that places different emphases on skills than by Tests and ODIs, the other formats. And a satisfactory answer to the latter question depends on how you answer the former. How do you take this forward?
Satyam Mukherjee, a postdoctoral fellow at the Kellogg School of Management, Northwestern University, has an answer. He has extended network analysis, a tool conventionally used to analyzing social media interactions, to cricket. On a social network like Facebook, people are treated as nodes and the connections between the nodes denote certain things about how the nodes are interacting. On a cricket ground, each team becomes one network in Mukherjee’s notebook, and the competition between them is a competition to be the better network.
Cricket is played by two teams at a time with 11 players per team. At all points during the game, teamwork is paramount although to varying extents. Even if one player fails to perform, the game could be lost by the team that player belongs to. Conversely, if one player plays too well, then the burden on the rest of the team is lighter.
In this scenario, network analysis provides a useful way to look not at players’ skills but how they’ve deployed them in situations that required their deployment.
In the second qualifier in IPL 2014, the Chennai Super Kings (CSK) trounced the Mumbai Indians (MI). Batting first, MI were restricted to 173 on a surface on which defending 190+ would’ve been easier, thanks to economic and incisive bowling from R. Ashwin and Mohit Sharma respectively.
Nonetheless, the sub-par score did require CSK to score at a stiff 8.7 runs-per-over (rpo) to win – and Suresh Raina became the man to do this, taking them to 176 in 18.4 overs at a rate of 9.4 rpo. Even though he had mowed down a sub-par score on a batting-friendly ground, he was awarded the ‘Man of the Match’ title, not Ashwin or Sharma. Why was that?
By addressing each game as a meeting of two networks that can interact only in specific ways, network analysis throws up four metrics that represent the quality of the interactions. They are
- PageRank – A proportional measure that describes the importance of a player
- In-strength – The sum of the fractions of runs a player has scored in partnership with others players
- Betweenness – Denotes the number of partnerships a batsman was involved in
- Closeness – Another proportional measure that describes the ability of a player to adapt to different batting positions (higher, middle, lower, etc.)
It’s reasonable that whichever player has made the most significant contribution to the values of those metrics deserves the ‘Man of the Match’ title. In the MI v. CSK game, Raina outperforms any other player from CSK, the winning team (from which the MoM is usually chosen).
Favoring batsmen, just like the game
Some things are immediately clear. One, PageRank and closeness are like global variables, with scores that can be carried and calculated across games (And while their definition seems arbitrary, their values do abide by well-defined formulas).
Two, all four metrics are relevant for batsmen and not bowlers or fielders. This is odd because it is the bowling side (same as the fielding side) that requires all the teamwork in the game. Does this mean Mukherjee’s approach is invalid? Not entirely because it is still useful in assessing how well batsmen have performed against certain oppositions and from certain batting positions.
For example, in the first qualifier in IPL 2014, KKR put in an all-round great performance to strangle KXIP and proceed to the finals. Ryan ten Doeschate (KKR) hit two sixes in the death overs to revive a sagging run rate and anchored two partnerships on the way. He takes the highest PageRank and betweenness in the game. Piyush Chawla scored the majority of the runs in the two partnerships he was involved in and has the highest in-strength. Finally, Robin Uthappa finished with the highest centrality, having played both as a upper-middle- and opening batsman in the tournament.
So much stands to reason, but this is where things get interesting because the ‘Man of the Match’ was Umesh Yadav.
How did this happen? A common woe among IPL teams is the performance of the ominously named death bowler, i.e. the player who bowls during the last four overs of a Twenty20 game. Over seven editions of the IPL, these so-called death overs have become notorious for the pace at which teams accrue runs in them. A death bowler, therefore, has to be good enough to stem the flow even if an in-form batsman is at the crease.
During the KKR v. KXIP game, Yadav bowled four overs for 13 runs (3.25 rpo) and took three wickets – Sehwag’s, Maxwell’s and Bailey’s. Moreover, one was a death over in which he conceded the princely sum of 1 run. These are match-winning feats in a Twenty20 game, and Yadav more than deserved to become the ‘Man of the Match’.
For the final game, Mukherjee drew up a network visualization (above) of the batting partnerships of KXIP and KKR. The nodes are colored according to their betweenness centrality. The size of each node is proportional to its PageRank. The colors of the connections are according to the colors of the source nodes. “For example, if we see the connection between Uthappa and Gambhir, Gambhir has a larger share of the runs they scored,” he explained.
Bowling performances notwithstanding: In IPL 2014, “for a majority of the matches, the Man of the Match compares well with the top three performers as per their centrality measures,” Mukherjee said. He said he hopes that such tools would work their way into extant decision-making procedures as a way to eliminate vested interests, biases and “close calls” as well as to help recruit new players. In the future, Mukherkjee plans to work something in to gauge bowlers and fielders, too.
Earlier, he had similarly analyzed the 2013 Ashes series held in and won by England. Then, the ‘Man of the Match’ awards agreed with his analysis of the games: Joe Root, Michael Clarke and Shane Watson, each of whom had higher in-strength and betweenness centrality than other players. He published his methods and results in Advances in Complex Systems in November 2013 (pre-print).