Today was day one of the Tokyo Grand Slam, one of the final events in the 2012 competition schedule.
Today the light weights took to the tatami and you might expect, the Japanese put on a marvelous display of Judo.
Looking at the data I collected form the IJF scoreboards, Ippon was awarded 36 times, with 23 remaining on the permanent record on http://ippon.org
This simple statistic tells us that on 13 occasions the referees awarded Ippon and later adjusted it down to a lesser score. Only 63% of the time did they stick with Ippon after awarding it. Now when looking at this number we have to be careful in reading too much into it and also not considering some of the factors affecting the data.
For example, the 36 number is derived by a simple search for records in the database. The database is populated by a piece of software that waits for a change in the scoreboard and writes one record each time the scores change. So a simple mistaken key press could be an instance here; not just when the referees or commission change a score.
We are also assuming that the 36 and 23 figures are accurate. Both could be wrong, they have not been verified.
Also you need to consider that this is data taken from one day of competition at a specific event. We are at the end of a competition year, post Olympic Games and in Japan. So for example Rishod Sobirov fought up a weight (as is Iliadis Illiadis later in the week). This affects the way people are fighting and the seeding. Obviously Japan has a large team, though it also features younger new players on the scene along with some of their seasoned players.
But this sort of snapshot data is useful, especially if we collect more and more of it over time and are able to grow large reliable sample groups to work with. Today for example the data grew by 296 records, which can be added to the existing data from the few events I have tested the system at to create a set of over 2000 scoreboard actions.
This data could potentially be used for example, to examine when scores are modified most often. The data contains the scoreboard clock data and the actual local time. So we could potentially identify patterns where errors occur. So perhaps (and this is conjecture) corrections happen most at 11:30am, which is perhaps halfway through the elimination rounds. We could use this data to suggest to event organisers a rest period for officials from 11:20 to 11:40 as the data might (again this is purely imaginary) suggest that people are making errors through tiredness.
We could easily discover the nations that score at the start of matches with big scores and those who do so later in matches. We could follow individual players scores over time and see trends in when they score and when they get scored against. Coaches might find this sort of data valuable in their planning.
In summary, collecting this data has proven to be fairly simple and recording it is a database where the information can be analysed is an important next step, one which I hope others will be interested in and maybe after reading this post might contact me with exciting ideas on how the data might be used. If that person is you, then please drop me an email to firstname.lastname@example.org