Hi,

trying to build upon your work a little bit. Can you help me understand how exactly are evaluations calculated? Looking into raw csv data, I noticed that first eval value is always positive, but varies:

df_cpwn['evals']=df_cpwn['Evaluations List'].apply(lambda s: list(map(int, s.strip('[]').split(','))))

df_cpwn.evals.apply(lambda x: x[0]).describe()


count    7568.000000
mean       39.310386
std         6.811494
min        16.000000
25%        35.000000
50%        39.000000
75%        44.000000
max        87.000000
Name: evals, dtype: float64

So first eval cannot be move 1, but it also cannot be something like move 10, since it's hard to believe that in 7k games black not even once had any advantage on that move.

edit: TIL, Stockfish evaluates the initial position as something random between 16 and 87.

Not all games in 15982_games_with_centipawn_metrics.csv contain the analysis of the positions. I can write some code to prevent these games from being inserted into the csv, which would prevent the inconsistent formatting. But I want to make sure their inclusion is not intentional before I try to pull the update.

Create plots using tools such as seaborn to automatically generate the data plot of players

I suggest a dispersion plot of Cp loss vs. Rating for each player, calculating the tendency line and the R^2 to check the correlation. It's clear from the results that Niemann has little or no correlation between Cp loss and rating.

Seaborn has all the tools to do all that automatically and generate beautiful plots.

after tinkering your script a bit, i have expected results and it works with several engines .. i suppose all (proper) UCI engines can be used, also those which can do Multi-PV, but are such PV lines included in the results? I mean, many positions can have many 'good' moves with almost same eval ..

Great analysis. I just had a quick look at average number of 0-CPL moves per game, and Niemann is coming out higher than the other players here. The 0-CPL moves are going to be more indicative of cheating.