Analyzing My Chess Data with R

Introduction

I enjoy playing on Chess.com and Lichess under the moniker iEnjoySomeChess. (Challenge me!) I’m an amateur chess player, but I’ve been following high-level chess tournaments since 2016, when Magnus Carlsen defended his world championship title against Sergey Karjakin in New York City. I began playing on Chess.com and Lichess in 2016 and 2017, respectively, so I have years of data, which I will analyze using tidyverse, lubridate, and chessR in RStudio.

Download my .Rmd file here.

ChessR: Extracting Data

I’m using the package ChessR to extract data from Chess.com and Lichess. The ChessR functions query an API, which is rate limited; the limit is unknown for Chess.com, but the limit is 15 games per second for Lichess. Then, I write the data to CSV’s, so that I can quickly load the data in the future.

# Chess.com
my_chesscom_data_raw <- get_game_data(usernames = 'iEnjoySomeChess')

write_csv(my_chesscom_data_raw,
          file = 'my_chesscom_data_raw.csv',
          na = 'NA')

# Lichess
my_lichess_data_raw <- get_raw_lichess(player_names = 'iEnjoySomeChess')

write_csv(my_lichess_data_raw, 
          file = 'my_lichess_data_raw.csv',
          na = 'NA')

Cleaning the Data

Chess.com

Extracting the data from Chess.com, there are 36 variables, and each row has information about a single game. I keep, rename, and edit the core variables, and I create other useful variables:

• site: In this case, Chess.com.
• event: The time category determined by time_control. For example, bullet (fast), blitz (fast but slower than bullet), rapid (slow and slower than blitz).
• time_control: The time allotted to each player, with the number of minutes and the increment (time in seconds added to a player’s clock after each move). For example, 10+0.
• white: The player who played with the white pieces. For example, iEnjoySomeChess.
• white_elo: The rating of the player who played with the white pieces.
• black: The player who played with the black pieces. For example, iEnjoySomeChess.
• black_elo: The rating of the player who played with the black pieces.
• rating_difference: The absolute value of the rating difference between white_elo and black_elo.
• termination: The ending of the game. For example, time, checkmate, resignation, among other values.
• n_moves: The number of moves in the game, noting that both players each making one move counts as one move.
• result: The result with format “white-black”, where a loss is zero points, a draw is half of a point, and a win is one point. For example, 1-0.
• opening: The opening, if the opening was recognized as a book opening on Chess.com. For example, Pawn-Opening-Accelerated-London-System.
• ECO: The code (a shorthand) for the opening, if the opening was recognized as a book opening on Chess.com. For example, A00.
• opponent: The opponent of the user whose games you requested.
• opponent_rating: The rating of the opponent of the user whose games you requested.
• user: The user whose games you requested. In this case, iEnjoySomeChess.
• user_color: The color of the pieces played by the user whose game you requested.
• user_result: The result of the game from the perspective of user. For example, win, loss, or draw.
• user_rating: The rating of the user whose games you requested.
• time_UTC: The date and time (UTC) at the beginning of the game. For example, 2022-08-23 21:22:52.
• end_time_UTC: The date and time (UTC) at the end of the game. For example, 2022-08-23 21:36:19.
• time_CDT: The date and time (CDT) at the beginning of the game. For example, 2022-08-23 16:22:52.
• end_time_CDT: The date and time (CDT) at the end of the game. For example, 2022-08-23 16:36:19.
• previous_time_CDT: The date and time (CDT) at the beginning of the previous game played by the user. For example, 2022-08-23 16:06:06.
• time_diff: The difference in time between previous_time_CDT and time_CDT.
• hour_interval: The hour (CDT) during which the game was played. For example, 16:00 if the game was played at 2022-08-23 16:06:06.
• link: The link to the game on Chess.com.

my_chesscom_data %>% 
  glimpse()

## Rows: 7,423
## Columns: 28
## $ site              <chr> "Chess.com", "Chess.com", "Chess.com", "Chess.com", …
## $ event             <fct> rapid, rapid, rapid, rapid, rapid, rapid, blitz, rap…
## $ time_control      <chr> "10+0", "10+0", "10+0", "10+0", "10+0", "10+0", "3+2…
## $ date              <date> 2022-08-23, 2022-08-23, 2022-03-04, 2022-01-14, 202…
## $ white             <chr> "Piercepoo17", "iEnjoySomeChess", "abdfghjk", "iEnjo…
## $ white_elo         <dbl> 1818, 1804, 699, 1804, 1742, 1778, 1794, 1798, 1757,…
## $ black             <chr> "iEnjoySomeChess", "Piercepoo17", "iEnjoySomeChess",…
## $ black_elo         <dbl> 1804, 1818, 1804, 1693, 1795, 1784, 1642, 1771, 1774…
## $ rating_difference <dbl> 14, 14, 1105, 111, 53, 6, 152, 27, 17, 2, 37, 30, 1,…
## $ termination       <chr> "repetition", "checkmate", "checkmate", "abandonment…
## $ n_moves           <dbl> 24, 31, 17, 56, 6, 69, 21, 29, 15, 34, 32, 38, 52, 4…
## $ result            <chr> "1/2-1/2", "0-1", "0-1", "1-0", "0-1", "0-1", "1-0",…
## $ opening           <chr> "Geet-Opening-1...d5-2.d3-Nf6", "Gambit-Declined-Que…
## $ ECO               <chr> "A00", "D31", "D00", "D13", "D02", "B21", "D85", "B5…
## $ opponent          <chr> "Piercepoo17", "Piercepoo17", "abdfghjk", "Timurka55…
## $ opponent_rating   <dbl> 1818, 1818, 699, 1693, 1742, 1778, 1642, 1798, 1774,…
## $ user              <chr> "iEnjoySomeChess", "iEnjoySomeChess", "iEnjoySomeChe…
## $ user_color        <chr> "black", "white", "black", "white", "black", "black"…
## $ user_result       <chr> "draw", "loss", "win", "win", "win", "win", "win", "…
## $ user_rating       <dbl> 1804, 1804, 1804, 1804, 1795, 1784, 1794, 1771, 1757…
## $ time_UTC          <dttm> 2022-08-23 21:22:52, 2022-08-23 21:06:06, 2022-03-0…
## $ end_time_UTC      <dttm> 2022-08-23 21:36:19, 2022-08-23 21:22:41, 2022-03-0…
## $ time_CDT          <dttm> 2022-08-23 16:22:52, 2022-08-23 16:06:06, 2022-03-0…
## $ end_time_CDT      <dttm> 2022-08-23 16:36:19, 2022-08-23 16:22:41, 2022-03-0…
## $ previous_time_CDT <dttm> 2022-08-23 16:06:06, 2022-03-04 22:58:12, 2022-01-1…
## $ time_diff         <Period> 16M 46S, 5m 18d 17H 7M 54S, 1m 18d 1H 9M 59S, 1d …
## $ hour_interval     <chr> "16:00", "16:00", "22:00", "21:00", "16:00", "14:00"…
## $ link              <chr> "https://www.chess.com/game/live/55028774859", "http…

Lichess

Extracting the data from Lichess, there are 23 variables, and each row has information about a single game. Again, I keep, rename, and edit the core variables, and I create other useful variables, with significant overlap with Chess.com; however, Lichess lacks n_moves, end_time_UTC, and end_time_CDT, while having some additional information:

• white_rating_change: The rating change of the player with the white pieces following the termination of the game.
• black_rating_change: The rating change of the player with the black pieces following the termination of the game.
• user_rating_change: The rating change of the user whose game you requested following the termination of the game.

my_lichess_data %>% 
  glimpse()

## Rows: 8,009
## Columns: 28
## $ site                <chr> "Lichess", "Lichess", "Lichess", "Lichess", "Liche…
## $ event               <fct> rapid, rapid, rapid, rapid, rapid, rapid, bullet, …
## $ time_control        <chr> "10+0", "10+0", "10+0", "10+0", "10+0", "10+0", "2…
## $ date                <date> 2022-10-12, 2022-10-11, 2022-10-11, 2022-10-11, 2…
## $ white               <chr> "iEnjoySomeChess", "Ibrahim780", "CleverRocky20", …
## $ white_elo           <dbl> 2175, 2137, 2245, 2172, 2077, 2183, 1893, 1887, 18…
## $ black               <chr> "adamnani", "iEnjoySomeChess", "iEnjoySomeChess", …
## $ black_elo           <dbl> 2137, 2177, 2190, 2219, 2193, 2028, 1938, 1905, 18…
## $ rating_difference   <dbl> 38, 40, 55, 47, 116, 155, 45, 18, 6, 93, 22, 3, 6,…
## $ termination         <chr> "Normal", "Normal", "Time Forfeit", "Normal", "Nor…
## $ result              <chr> "0-1", "1/2-1/2", "1-0", "1-0", "1-0", "1-0", "1-0…
## $ white_rating_change <dbl> -15, 1, 5, 18, 8, 10, 7, 6, 6, -4, -6, 6, -6, 15, …
## $ black_rating_change <dbl> 6, -2, -13, -6, -21, -3, -6, -6, -6, 5, 6, -6, 6, …
## $ opening             <chr> "Nimzo-Indian Defense: Classical Variation", "Sici…
## $ ECO                 <chr> "E32", "B90", "B23", "D37", "D03", "E32", "D20", "…
## $ opponent            <chr> "adamnani", "Ibrahim780", "CleverRocky20", "Divisi…
## $ opponent_rating     <dbl> 2137, 2137, 2245, 2219, 2077, 2028, 1938, 1905, 18…
## $ user                <chr> "iEnjoySomeChess", "iEnjoySomeChess", "iEnjoySomeC…
## $ user_color          <chr> "white", "black", "black", "white", "black", "whit…
## $ user_result         <chr> "loss", "draw", "loss", "win", "loss", "win", "win…
## $ user_rating         <dbl> 2175, 2177, 2190, 2172, 2193, 2183, 1893, 1887, 18…
## $ user_rating_change  <dbl> -15, -2, -13, 18, -21, 10, 7, 6, -6, -4, 6, 6, 6, …
## $ time_UTC            <dttm> 2022-10-12 14:33:07, 2022-10-11 18:22:08, 2022-10…
## $ time_CDT            <dttm> 2022-10-12 09:33:07, 2022-10-11 13:22:08, 2022-10…
## $ previous_time_CDT   <dttm> 2022-10-11 13:22:08, 2022-10-11 09:34:08, 2022-10…
## $ time_diff           <Period> 20H 10M 59S, 3H 48M 0S, 1H 37M 24S, 12H 14M 46S…
## $ hour_interval       <chr> "09:00", "13:00", "09:00", "07:00", "19:00", "19:0…
## $ link                <chr> "https://lichess.org/bQmZJs4r", "https://lichess.o…

Analysis

While I want to compare data between Chess.com and Lichess, I first want to perform separate analysis for Chess.com and Lichess. I will analyze five main questions:

1. How do my games end when I lose, draw, and win?
2. How often do I play with the white pieces, and how often do I play with the black pieces?
3. On average, how much rating do I gain/lose according to color of pieces? According to month?
4. What is my longest break from playing on Chess.com? Lichess?
5. What times of day am I most active?

After I perform separate analysis for Chess.com and Lichess, I will combine the data sets, and I will perform a combined analysis for Chess.com and Lichess.

Chess.com

Game Termination

Q: “How do I win games?”

First, let’s check how many games I’ve won in bullet, blitz, and rapid. (Note: I have only played bullet, blitz, and rapid games on Chess.com, but I’ve played more time controls on Lichess, includingclassical and chess960. For the most part, I limit my attention to bullet, blitz, and rapid.)

I win a significant portion (74.4%) of bullet games by time, which makes sense given that bullet is faster than blitz and rapid, where time is less of a factor. It’s interesting that abandonment becomes more important in blitz and rapid (with values of 1.83% and 2.17%, respectively), as opponents may start a game, but leave due to the time committment. Lastly, whereas my method of winning games is somewhat uniform for blitz, I win a significant portion (71.7%) of rapid games by resignation.

Q: “How do I lose games?”

First, let’s see how many games I’ve lost across bullet, blitz, and rapid.

I lose a significant portion (61.9%) of bullet games by time, which, again, makes sense given that bullet is faster than blitz and rapid, where time is less of a factor. Further, whereas my methods of winning blitz games were more uniform, I lose a large portion (56.2%) of blitz games by time.

Q: “How do I draw games?”

First, let’s see how many games I’ve drawn across bullet, blitz, and rapid.

I draw a large portion (57.8%) of my bullet games by timeout vs. insufficient material, which indicates that in my drawn bullet games there are usually no remaining pieces on the board. Mostly, I do not draw my games by 50-move rule. Further, my methods of drawing blitz games are somewhat uniformly distributed, whereas I draw a large portion (47.8%) of my rapid games by agreed draw.

Color of Pieces

Q: “Do I play with the white or black pieces more?”

I would expect to play with the white and black pieces with uniformly random probability, that is, 50%, which is approximately true, though for rapid I tend to play with the black pieces (52.9%) more than the white pieces (47.1%).

Rating Gain/Loss

As stated in Cleaning the Data, the data for Chess.com does not have information about rating change, but we can create a user_rating_change column, just like Lichess, since we have a complete record of user games, which includes user_rating. (Note: Chess.com and Lichess provide separate ratings for different values of event; thus, we have to sort by event, and subsequently time_CDT, before finding the difference between successive values of user_rating.)

Q: “On average, how much rating do I gain/lose with the black and white pieces?”

I would expect to gain more rating with the white pieces, rather than the black pieces, since white plays the first opening move, and thus has the first chance to create an advantage, so it’s suprising that I gain more rating (it’s even suprising that I don’t lose rating) with the black pieces. Further, I tend to gain more rating from rapid than I gain from bullet or blitz. I have not played as many rapid games as bullet and blitz games, so perhaps my rating is cathcing up to my skill level.

Q: “On average, how much rating do I gain/lose in a given month?”

I tend to lose rating around February, whereas other months are mostly consistent, or I gain rating. I don’t have an explanation for the rating loss in February.

Number of Moves

The extracted data from Chess.com has information about the number of moves (n_moves) for each game. In addition to the five main questions stated under analysis, I include the following question:

Q: “How long are my games in number of moves?”

My games are typically between 30 and 50 moves, with some notable outliers. The outlier game with 142 moves is a game I played with a friend at UW-Madison, where I walked my king, being pursued by a queen, from h8 to b1 to h8, and I went on to win the game!

Longest Break

Q: “What’s the longest break I’ve taken from playing on Chess.com?”

My longest break from Chess.com was 6 Months and 12 Days, which time elapsed between April 13, 2016 and October 26, 2016. Considering I started playing on Chess.com in January 2016, said break was probably a natural waning of interest, since I wasn’t as enamored with chess then as I am now.

Time of Day

Q: “What times of day am I most active?”

I have not played any games at 23:00 or 24:00 (00:00), and I play a lot of games in the evening, between 16:00 and 19:00. I tend to play rapid games either in the morning (08:00 to 09:00) or the evening (16:00 to 19:00).

Lichess

Game Termination

The exported Lichess data only distinguishes between Normal and Time Forfeit for the variable termination, so we don’t have the same breakdown as Chess.com. As such, I exclude classical (longer time control), since Time Forfeit is not a strong factor for classical. (Note: Again, I did not play classical games on Chess.com.)

Q: “How do I win games?”

First, let’s see how many games I’ve won across bullet, blitz, and rapid.

Again, I win a large portion (52.5%) of bullet games by Time Forfeit, which makes sense given that bullet is faster than blitz and rapid, where time is less of a factor, as evidenced by the decreasing percentage of Time Forfeit from bullet to blitz, and from blitz to rapid.

Q: “How do I lose games?”

First, let’s see how many games I’ve lost across bullet, blitz, and rapid.

Whereas I won less and less games by Time Forfeit from bullet to blitz to rapid, I lose a significant portion (62.3%) of rapid games by Time Forfeit. As I play rapid games with a time_control of 10+0 (no increment, that is, no time added after each move), I need to work on my time management.

Q: “How do I draw games?”

First, let’s see how many games I’ve drawn across bullet, blitz, and rapid.

The breakdown of drawn games on Lichess isn’t too useful, since it’s rare to draw games by Time Forfeit, unless no pieces remain on the board, which appears to occur mostly for bullet games on Lichess. We saw the same trend on Chess.com.

Color of Pieces

Q: “Do I play with the white or black pieces more?”

Again, I would expect to play with the white and black pieces with uniformly random probability, that is, 50%, which is approximately true, though for rapid I tend to play with the white pieces (54.4%) more than the black pieces (45.6%); more suprising, I have only played 8 classical games, of which 6 (75.0%) were with the black pieces and 2 (25.0%) were with the white pieces.

Rating Gain/Loss

Q: “On average, how much rating do I gain/lose with the black and white pieces?”

I tend to lose rating with the black pieces on Lichess, whereas I tend to gain rating with the white pieces on Lichess. Compared to Chess.com, this is more consistent with my expectations.

Q: “On average, how much rating do I gain/lose in a given month?”

I tend to lose rating during May, June, and July, whereas I tend to gain rating in other months. What’s up with summer?

Longest Break

My longest break from Lichess was 4 Months and 6 Days, which time elapsed between March 7, 2022 and July 14, 2022. Really, this year? I went to Alaska in June; otherwise, I don’t have an explanation for the break. Zugzwang with my courses (quantum mechanics, thermal physics, and laboratory), perhaps?

Time of Day

Q: “What times of day am I most active?”

I tend to play rapid games on Lichess much earlier in the day (07:00, 08:00) than I do bullet or blitz games. Otherwise, the times are somewhat uniform during mid-day, and they dropoff around nighttime hours.

Comparison

Now, I will combine the data and perform a combined analysis for Chess.com and Lichess. I drop the columns unique to each data set; then I bind the data sets, so there is one large data set. (Note: The percentages will now be over all games, from both Chess.com and Lichess.)

Start Date/Total Games

Q: “When did I start playing on either site?”

Q: “How many games have I played on each site? On which site have I played more games?”

I started playing on Chess.com, but I now play primarily on Lichess. My total number of games played on Lichess has surpassed my total number of games played on Chess.com.

Time of Day

Q: “What times of day am I most active by site?”

Rating

Q: “What is my highest rating achieved on either site by event?”

Lichess has higher ratings than Chess.com, and on average my rating is around 300-400 points lower on Chess.com.

Conclusion

As always, there’s a lot of data, and there’s a lot more to do with the data. For example, I could generate further insights by filtering the data according to year, which could be useful, since my chess skills have improved over time, and thus my above analysis might not reflect the most recent trends in my chess games. Nonetheless, the above analysis provided useful insights, and I plan to update and revisit my chess data in the future.