Predicting Rapid Ratings on Chess.com: A Data-Driven Approach

Greetings, fellow chess enthusiasts!

I love playing chess and have been playing for almost 8 years. As many of us strive to improve our chess skills, one fascinating question arose in my mind: Can we predict a player's Rapid rating based on their Bullet, Blitz, and Tactics ratings? To address this curiosity, I went on a journey of data exploration and statistical modelling using Chess.com's publicly available data.

Using Chess.com API, I collected 628 data observations of players from the top 10 most active countries on Chess.com so that I have a heterogeneous sample without any bias. Each observation included a player's current Bullet, Blitz, Rapid, and Tactics ratings.

Starting with a linear regression model, I utilized the Bullet, Blitz, and Tactics ratings as predictor variables, with the goal of predicting each player's Rapid rating. After running the model, I found an R-squared value of 0.77, indicating that about 77% of the variability in Rapid ratings can be accounted for by these three predictors, and is a good indicator of the data, suggesting a strong linear relationship between these variables and the Rapid rating.

Delving deeper into the model, the ANOVA resulted in the following regression equation:
Predicted Rapid rating = 365.49 - 0.02 *( Bullet Rating) + 0.6624 * (Blitz Rating) + 0.101 * (Tactics Rating)  with ± 150 points error

For example,
My current Bullet rating is 1062
Blitz Rating is 1153
Tactics Rating is  2384

My Predicted Rapid Rating  = 365.49 - 0.02 *( 1062) + 0.6624 * (1153) + 0.101 * (2384)  with ± 150  = 1384 ± 150.  ( 1234 - 1534 ).

My Predicted Rapid Rating = ( 1234 - 1534 )
My Current Rapid Rating = 1500


To visualize the relationships and predictions more clearly, I created a scatter plot with Tactics and Blitz ratings on the vertical axes (left and right, respectively) and Rapid ratings on the horizontal axis ( discarded bullet since it these ratings are enough to make a relation). By choosing to interpolate with linear regression lines for both Tactics and Blitz ratings, I was able to generate two lines representing the predictive relationships between each of these variables and the Rapid rating.

This visualization effectively illustrates the comparative influences of Tactics and Blitz ratings on the Rapid rating. As seen in the plot, both Tactics and Blitz ratings contribute significantly to predicting a player's Rapid rating, but their effects are not identical.

By analyzing and visualizing the Chess.com data in this way, we can gain a clearer understanding of how different aspects of a player's performance relate to their Rapid rating.

As chess players and enthusiasts, we can leverage such data-driven insights to better understand the game and perhaps even inform our strategies for improvement. Happy gaming, and may your next move lead you to victory!  

My Ultimate Goal is to use statistical modelling on my games to find which Chess openings I need to work on.
PS: For more details on how exactly I did, I wrote about it here https://medium.com/@tusharsharma_505/predicting-rapid-ratings-on-chess-com-a-data-driven-approach-666f33701a6