About the statistics
The automation of the results processing for the league has meant that it is relatively straightforward to produce a range of statistics for players, pairs, teams and clubs. Hopefully at least some players will find them interesting, but please bear in mind that they are provided for entertainment value only, and unlike the league tables, no guarantee is made that they will be accurate.
Conceded rubbers, whether through injury or absence of players or through concession or abandonment of a match, are not counted in the statistics. However, incomplete rubbers where one team reaches 7 games are included, as per rule 11.3.
If you do find them interesting or have any suggestions for how they could be improved, please let us know by using the contact page.
Individual players are ranked using a figure called the Wilson score, which is a measure of an individual's performance across the whole season. It is intended to be a compromise between using total games won and average games won as comparators, as both of these have disadvantages. A player might score a high number of total games by playing lots of matches for different teams within his club, despite a relatively low average. Conversely, a player might turn up for just one or two matches, score over 30 games, and be the top scorer when measured by average games.
The Wilson score rewards those who play consistently and score highly throughout the season. A player who scores an average of 9 games per rubber across 10 matches will be ranked higher than someone with an average of 8.8 but who manages to play 11 matches. They will also be ranked above someone who has an average of 11 games but only plays 4 matches during the season.
The score is nominally out of 100 although players who score extremely highly and consistently throughout the season may find it possible to get a score higher than that. An average of about 9.4 games across 14 matches would result in a score of 100.
It is inspired by the Wilson confidence interval, used in statistics to determine the level of confidence that a given result is valid (although the implementation here is somewhat different). Put briefly, the more matches a player has played, the more confident we can be that their average game score is a true reflection of their ability, rather than just a fluke or stroke of bad luck!
The formula used is 12.5 * RubbersPlayed * AverageGames / ( RubbersPlayed + 7.5 )