I often find myself frustrated by the various fantasy cricket games that seem to score players on arbitrary (and often meaningless) systems that don’t really reflect an actual game of cricket. They also often place restrictions on picking players, (for example - enforcing that you need 5 batsmen, an all-rounder, a keeper and four bowlers.)
In real life, which in my opinion fantasy games should aim to replicate, a team isn’t restricted in such a way and players can change roles depending on what best suits the team.
With this in mind, I designed a simple model based on the simple principles of Test cricket...
To win a match of test cricket a team needs to make more runs and take the same amount or more wickets (unless there are some strange declarations) than the opposition.
With this in mind, I came up with the following formula to use as a scoring system:
(Wickets Taken - Wickets Lost) x (Runs Scored - Runs Conceded)
I thought this was a simple, yet accurate way of determining a player’s performance in the context of a Test match.
The simple idea was that if a team either took fewer wickets than they lost or made fewer runs than their bowlers conceded then their ‘rating’ would be negative. Hence, the idea was to balance taking wickets and scoring runs, so a team of just bowlers would get a negative score, as would a team of just batsmen.
Initially, I was happy with this version of the model, although I realised that fielding was not taken into consideration and so the model would not pick a wicket-keeper unless they deserved to be picked as a batsman in their own right. As such, a few tweaks were made to the model to encourage a keeper to be picked and to reward players who take catches. Catches were treated as 10% of a wicket, so a player who took 10 catches for the year was equivalent to a bowler taking 1 wicket. Also, a stumping was counted as a wicket for the bowler and the keeper to further enhance a keeper’s value.
Once this formula had been refined, all the Test players who made an appearance in 2019 were entered into the formula and the only constraints the model was given were that there needed to be 11 players selected and no player could be chosen more than once. There was no need to tell the model it had to pick four bowlers and a keeper, as I was confident the model would succeed in picking a balanced side because the scoring formula it was based on was designed to select a side with an inherent balance of batsmen and bowlers.
The following was the Test XI that the model chose:
The Model’s team is quite balanced, with five pure batsmen, a keeper-batsman, an allrounder and four bowlers. Obviously the model can’t differentiate between fast bowlers and spinners so no spinner is selected because none had better records than the fast bowlers selected , which is not necessarily a bad selection policy.
BATSMEN: The main surprise selection is David Warner, who had a miserable Ashes campaign and as such would not have made many World XIs picked by experts. He saved his year with a brilliant start to the Australian summer (and accumulating almost half his runs in one inning) to end the year with a respectable average of almost 50.
It is important to note that his average is only ranked 20th for the year (from players with at least three matches) and yet the model still chose him as one of the best batsmen. This shows that the model values a balance between total runs scored and batting average , as total value added to the team is what the model bases its selection on.
This means that a player who averaged 100 but only made 100 runs from one innings would find it hard to be selected because their overall value to the team would be quite limited. This also explains why Warner was selected - his average is solid and he made the 8th most runs for the year of all Test players.
Apart from Warner, it is hard to argue with any of the other batting selections.
While I’m sure a lot of people will be wondering why Virat Kohli or Rohit Sharma aren’t in the team, this is again due to the fact that their aggregate of runs isn't as high as other players selected in the team. Although their averages may have been as good as, or even better than, other players, they didn’t make as many runs and so provided less overall value to their team.
In a way, the Indian batsmen were disadvantaged due to how dominant their team was in 2019 - Kohli played eight matches but only batted 11 times and so lost opportunities to add to his run tally that players from Australia and England received.
In any case, Agarwal, Labuschagne, Smith and Rahane all had brilliant years and it is hard to argue that any of them don’t deserve to be in any team of the year.
WICKETKEEPER & ALL-ROUNDER: It is interesting to note that the model picked a single keeper and a single allrounder of its own accord, without needing to artificially restrictions or obligations around these positions, showing that the formula the model is based upon is suited to giving a naturally balanced team.
The battle for the keeper position was between Quinton de Kock and BJ Watling and while Watling averaged more, de Kock made more runs and had more dismissals and as such was preferred by the model.
Jason Holder’s all-round performances from just five matches were good enough to have him in the team as the sole West Indian. Ben Stokes could perhaps count himself unlucky but is probably remembered in 2019 due to some specific incredible performances as opposed to a having a dominant year - as his overall record (Bat av: 45.6 / Bowl av: 35.5) doesn’t compare to Holder’s.
(Holder is also the only player in the 2019 team who was also picked in the model’s 2018 XI.)
BOWLERS: The bowlers selected reflect how many quality bowlers there were in 2019 with outstanding records.
I’m sure most people looking at this team will be able to think of a couple of very unlucky players who weren’t picked, but again, this is a heartless model that simply picks the team that will provide the best value as implied by the formula it is built on. As such, the bowlers picked in this team must reflect the best value when combined with the batsmen selected.
Firstly, it is important to note that all four bowlers had outstanding years, each with averages under Mitch Starc’s 20.7, and as such justify selection in their own right. The other factor which helped each of these bowlers is that even if their batting average wasn't very high, they were not required to bat often or weren't dismissed often, meaning they didn’t lower their team’s batting performance. This is probably the reason that Pat Cummins wasn’t picked despite his incredible 59 wickets at 20.13. Cummins was dismissed 13 times compared to Starc’s four and so in the model’s mind, this is the equivalent to 9 more wickets added to Starc’s bowling tally, which further lowers his bowling average.
(Note: This is a potential weakness of the model, as it puts the same weight on every player’s batting performance regardless of where they are in the batting order.)
The bowling averages of Cummins and the other bowlers selected are similar however, and so perhaps using their batting as a tiebreaker is not the worst idea - and on often used by "real life" selectors. Mohammed Shami is another good example of this and his batting record shows how dominant India were this year - Shami played eight Test matches but only batted four times and was only dismissed twice.
Overall - I think the model’s team is a very good team and it shows that a simple formula can be used to select a Test match team using the principles of what is required to win a Test match.
Obviously the model is very simplistic and therefore has some clear weaknesses in its selection policy and would not be recommended to be used to select an actual Test team, although it shows that this model can help guide selection if not determine it on its own.
Further complexity could be added to the model, such as enforcing the selection of a spinner or changing the way bowler’s batting records are implemented, but the purpose of the model at this stage is to reflect the simplicity of how to win a Test match and picking a team with the fewest constraints possible.
Oliver Fitzpatrick is a cricket tragic and Carlton FC (even more) tragic and can usually be found in the Members Pavillion at the MCG. When he’s not at the ‘G he’s studying a Masters of Statistics and Operations Research at RMIT and tweeting his thoughts @ocfitz1.