Kusya1/Lichess_Games_and_Metrics

♛ Chess Evaluation-Based Feature Dataset

Dataset Description

This dataset contains over 5 million chess games sourced from Lichess.org, containing Stockfish Engine Evaluations and clock timings and moves and game metadata.

The dataset is composed of two files:

• lichess_games_moves: Contains traditional PGN-style metadata and move sequences, stored in Parquet format for fast querying.

• lichess_moves_sample.parquet: Contains per-player, per-game performance metrics derived from evaluations and timing data, including centipawn loss, move accuracy, and time usage.

Each game is uniquely identified by a game_id (UUID), which allows for seamless joins between the two datasets.

This structure enables researchers to analyze player behavior at both the game level and the player level, supporting tasks such as cheat detection, player profiling, skill progression analysis, and AI performance benchmarking. The dataset spans a wide range of game types, rating tiers, and event formats, offering a granular view into decision-making, precision, and time management across millions of chess games.

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📊 Dataset Summary

Attribute	Description
Total Games	5,000,000
ELO Range	400 to 3945
Time Controls	Bullet (21%), Blitz (47%), Rapid (29%), Classical (2%), Ultrabullet (0.7%)
Unique Openings	142 openings, 100 variations
Event Types	Game (88%), Tournament (11%), Swiss Tournament (1%)

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📌 Features

Field	Description	Calculation
total_cpl	Total centipawn loss	sum(abs(eval_after - eval_before))
acpl	Average centipawn loss	total_cpl / move_count
mcpl	Median centipawn loss	median(abs(eval_after - eval_before))
std_cpl	Std deviation of CPL	std(abs(eval_after - eval_before))
25%_cpl	25th percentile CPL	percentile(abs(eval_after - eval_before), 25)
75%_cpl	75th percentile CPL	percentile(abs(eval_after - eval_before), 75)
min_cpl	Minimum CPL	min(abs(eval_after - eval_before))
max_cpl	Maximum CPL	max(abs(eval_after - eval_before))
move_cnt	Count of evaluated moves	Count of moves with evaluations
inaccuracy_cnt	Moves with win prob drop 5–10%	5 < delta_wp < 10
mistake_cnt	Moves with win prob drop 10–15%	10 < delta_wp < 15
blunder_cnt	Moves with win prob drop ≥15%	delta_wp ≥ 15
best_cnt	Moves with win prob drop ≤5%	delta_wp ≤ 5
perfect_streak	Longest best-move streak	max(consecutive_moves where delta_wp ≤ 5)
mean_acc	Mean move accuracy	mean(accuracy_scores)
med_acc	Median accuracy	median(accuracy_scores)
std_acc	Std deviation of accuracy	std(accuracy_scores)
25%_acc	25th percentile accuracy	percentile(accuracy_scores, 25)
75%_acc	75th percentile accuracy	percentile(accuracy_scores, 75)
min_acc	Minimum accuracy	min(accuracy_scores)
max_acc	Maximum accuracy	max(accuracy_scores)
mean_win%	Mean win probability	mean(win_probs)
med_win%	Median win probability	median(win_probs)
std_win%	Std deviation win prob	std(win_probs)
25%_win%	25th percentile win prob	percentile(win_probs, 25)
75%_win%	75th percentile win prob	percentile(win_probs, 75)
min_win%	Minimum win probability	min(win_probs)
max_win%	Maximum win probability	max(win_probs)
avg_tpm	Avg time per move (sec)	mean(move_times)
med_tpm	Median time per move	median(move_times)
stdev_tpm	Std deviation of time	std(move_times)
25%_tpm	25th percentile time	percentile(move_times, 25)
75%_tpm	75th percentile time	percentile(move_times, 75)
min_tpm	Minimum time per move	min(move_times)
max_tpm	Maximum time per move	max(move_times)
instant_move_cnt	Moves made in ≤1s	count(time ≤ 1)
game_id	Unique game identifier	UUID format
player	Player color	white or black
username	Player username	From Lichess
elo	Player rating	ELO score
rating_diff	Rating change	Post-game change
title	Chess title	e.g., GM, IM, CM
Site	Game URL	Lichess link
Date	Local game date	YYYY.MM.DD
Round	Game round	Often -
Result	Game result	1-0, 0-1, or 1/2-1/2
UTCDate	UTC date	Game timestamp
UTCTime	UTC time	Game start time
ECO	ECO code	e.g., A00, B01
Opening	Opening name	Named variation
TimeControl	Raw control format	e.g., 600+0
Termination	End reason	e.g., Normal, Timeout
timecontrol_type	Time class	Bullet, Blitz, etc.
event_type	Event format	Game, Tournament, Swiss
event_url	Tournament link	If applicable

• Win Probability: WP(cp) = 50 + 50 × ( 2 / (1 + e^(−0.00368208 × cp)) − 1 )

• Accuracy: ΔWP = WP_before − WP_after Accuracy(ΔWP) = clamp( 103.1668 × e^(−0.04354 × ΔWP) − 3.1669 , 0, 100 )

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🔍 Sample Observation from lichess_metrics_sample.parquet

{
    "total_cpl": 1436.0,
    "acpl": 27.615384615384617,
    "mcpl": 10.0,
    "std_cpl": 75.82777901934035,
    "25%_cpl": 4.0,
    "75%_cpl": 16.25,
    "min_cpl": 0.0,
    "max_cpl": 474.0,
    "move_cnt": 52,
    "inaccuracy_cnt": 0,
    "mistake_cnt": 0,
    "blunder_cnt": 2,
    "best_cnt": 50,
    "perfect_streak": 37,
    "mean_acc": 95.04878233982011,
    "med_acc": 99.06330947144633,
    "std_acc": 14.906050942960489,
    "25%_acc": 97.21119376075846,
    "75%_acc": 100.0,
    "min_acc": 16.396635484120793,
    "max_acc": 100.0,
    "mean_win%": 75.49193923600627,
    "med_win%": 85.45913904456496,
    "std_win%": 18.60559724644749,
    "25%_win%": 52.46040066101623,
    "75%_win%": 87.16445709539632,
    "min_win%": 35.17797456776559,
    "max_win%": 97.54474363414323,
    "avg_tpm": 13.257142857142858,
    "med_tpm": 12.0,
    "stdev_tpm": 7.904222586306483,
    "25%_tpm": 7.0,
    "75%_tpm": 20.0,
    "min_tpm": 2.0,
    "max_tpm": 29.0,
    "instant_move_cnt": 0,
    "game_id": "3c2687ff-b368-4ab0-8018-05346ccd16e9",
    "player": "white",
    "username": "TIBURONCHILENO",
    "elo": "2366",
    "rating_diff": "+1",
    "title": "FM",
    "Site": "https://lichess.org/v0oGqi2g",
    "Date": "2025.02.20",
    "Round": "-",
    "Result": "1-0",
    "UTCDate": "2025.02.20",
    "UTCTime": "00:48:50",
    "ECO": "A45",
    "Opening": "Indian Defense",
    "TimeControl": "180+2",
    "Termination": "Normal",
    "timecontrol_type": "Blitz",
    "event_type": "tournament",
    "event_url": "https://lichess.org/tournament/oeFqsJi9",
    "moves":
}

🔍 Sample Observation from lichess_moves_sample.parquet

{
    "game_id": "3c2687ff-b368-4ab0-8018-05346ccd16e9",
    "Site": "https://lichess.org/v0oGqi2g",
    "Date": "2025.02.20",
    "Round": "-",
    "White": "TIBURONCHILENO",
    "Black": "Gran_Maestro2756",
    "Result": "1-0",
    "UTCDate": "2025.02.20",
    "UTCTime": "00:48:50",
    "WhiteElo": "2366",
    "BlackElo": "1940",
    "WhiteRatingDiff": "+1",
    "BlackRatingDiff": "-1",
    "ECO": "A45",
    "Opening": "Indian Defense",
    "TimeControl": "180+2",
    "Termination": "Normal",
    "BlackTitle": None,
    "WhiteTitle": "FM",
    "event_url": "https://lichess.org/tournament/oeFqsJi9",
    "timecontrol_type": "Blitz",
    "event_type": "tournament",
    "has_eval": "1",
    "moves": "1. d4 { [%eval 0.17] [%clk 0:03:00] } 1... Nf6 { [%eval 0.19] [%clk 0:03:00] } 2. Bf4 { [%eval 0.05] [%clk 
    0:03:01] } 2... e6 { [%eval 0.11] [%clk 0:03:01] } 3. e3 { [%eval 0.0]  [%clk 0:03:03] } 3... c5 { [%eval 0.0] [%clk 0:03:02] }
    4. Nd2 { [%eval 0.1] [%clk 0:03:04] } 4... cxd4 { [%eval 0.37] [%clk    0:03:03] } 5. exd4 { [%eval 0.15] [%clk 0:03:04] } 5... 
    b6 { [%eval 0.19] [%clk 0:03:04] } 6. c3 { [%eval 0.16] [%clk 0:03:06] }    6... Bb7 { [%eval 0.2] [%clk 0:03:05] } 7. Ngf3 { 
    [%eval 0.15] [%clk 0:03:07] } 7... d6 { [%eval 0.27] [%clk 0:03:06] } 8.    Bd3 { [%eval 0.25] [%clk 0:03:08] } 8... Be7 { [%eval
    0.29] [%clk 0:03:07] } 9. Qe2 { [%eval 0.26] [%clk 0:03:10] } 9... O-O {    [%eval 0.24] [%clk 0:03:07] } 10. h3 { [%eval 0.28] 
    [%clk 0:03:11] } 10... Nbd7 { [%eval 0.27] [%clk 0:03:08] } 11. O-O {   [%eval 0.27] [%clk 0:03:13] } 11... Re8 { [%eval 0.25] 
    [%clk 0:03:09] } 12. Rfe1 { [%eval 0.28] [%clk 0:03:14] } 12... Qc7 {   [%eval 0.28] [%clk 0:03:10] } 13. Ng5 { [%eval 0.14] 
    [%clk 0:03:15] } 13... h6? { [%eval 1.37] [%clk 0:03:10] } 14. Nxe6?? {     [%eval -1.66] [%clk 0:03:13] } 14... fxe6 { [%eval 
    -1.39] [%clk 0:03:04] } 15. Qxe6+ { [%eval -1.38] [%clk 0:03:15] } 15...    Kf8?? { [%eval 3.98] [%clk 0:03:05] } 16. Bg6?? { 
    [%eval -0.76] [%clk 0:03:15] } 16... Bd5 { [%eval -0.75] [%clk 0:02:58] }   17. Qf5 { [%eval -0.51] [%clk 0:03:16] } 17... Red8?
    { [%eval 2.48] [%clk 0:02:57] } 18. c4 { [%eval 2.62] [%clk 0:03:09] }  18... Bf7? { [%eval 4.93] [%clk 0:02:57] } 19. Bxf7 { 
    [%eval 4.89] [%clk 0:02:50] } 19... Kxf7 { [%eval 4.91] [%clk 0:02:58] }    20. Qe6+ { [%eval 4.88] [%clk 0:02:52] } 20... Kg6 { 
    [%eval 4.86] [%clk 0:02:58] } 21. Qxe7 { [%eval 4.64] [%clk 0:02:51] }  21... Re8 { [%eval 4.87] [%clk 0:02:56] } 22. Qxd6 { 
    [%eval 4.97] [%clk 0:02:52] } 22... Qxd6 { [%eval 4.86] [%clk 0:02:57] }    23. Bxd6 { [%eval 4.92] [%clk 0:02:45] } 23... Rad8 {
    [%eval 5.05] [%clk 0:02:56] } 24. Bc7 { [%eval 4.95] [%clk 0:02:43] }   24... Rc8 { [%eval 4.97] [%clk 0:02:56] } 25. Bg3 { 
    [%eval 4.8] [%clk 0:02:45] } 25... Nh5 { [%eval 4.99] [%clk 0:02:55] }  26. Bd6 { [%eval 5.09] [%clk 0:02:39] } 26... Nhf6 { 
    [%eval 4.95] [%clk 0:02:53] } 27. f3 { [%eval 4.82] [%clk 0:02:40] }    27... Rcd8 { [%eval 5.1] [%clk 0:02:46] } 28. Kf2 { 
    [%eval 4.96] [%clk 0:02:38] } 28... Nf8 { [%eval 5.29] [%clk 0:02:41] }     29. Rxe8 { [%eval 5.19] [%clk 0:02:37] } 29... Rxe8 { 
    [%eval 5.25] [%clk 0:02:41] } 30. Bxf8 { [%eval 5.0] [%clk 0:02:33] }   30... Rxf8 { [%eval 4.9] [%clk 0:02:42] } 31. Re1 { 
    [%eval 4.86] [%clk 0:02:34] } 31... Rd8 { [%eval 5.0] [%clk 0:02:43] }  32. d5 { [%eval 5.03] [%clk 0:02:36] } 32... Nd7 { 
    [%eval 5.37] [%clk 0:02:43] } 33. Ne4 { [%eval 4.42] [%clk 0:02:35] }   33... Ne5 { [%eval 4.39] [%clk 0:02:43] } 34. Rd1 { 
    [%eval 3.9] [%clk 0:02:14] } 34... Nxc4 { [%eval 3.75] [%clk 0:02:43] }     35. b3 { [%eval 3.66] [%clk 0:02:15] } 35... Nd6 { 
    [%eval 4.41] [%clk 0:02:41] } 36. Nxd6 { [%eval 4.36] [%clk 0:02:16] }  36... Rxd6 { [%eval 4.13] [%clk 0:02:42] } 37. Ke3 { 
    [%eval 4.35] [%clk 0:02:17] } 37... Kf5 { [%eval 4.57] [%clk 0:02:42] }     38. Kd4 { [%eval 4.42] [%clk 0:02:18] } 38... a6 { 
    [%eval 4.89] [%clk 0:02:41] } 39. Re1 { [%eval 4.77] [%clk 0:02:19] }   39... g6 { [%eval 5.07] [%clk 0:02:41] } 40. g3 { [%eval
    4.92] [%clk 0:02:19] } 40... h5 { [%eval 5.35] [%clk 0:02:39] } 41. h4 {    [%eval 5.32] [%clk 0:02:19] } 41... Kf6 { [%eval 5.3]
    [%clk 0:02:38] } 42. Re8 { [%eval 5.36] [%clk 0:02:18] } 42... Kf7 {    [%eval 5.08] [%clk 0:02:39] } 43. Rc8 { [%eval 5.24] 
    [%clk 0:02:19] } 43... Rf6 { [%eval 5.8] [%clk 0:02:35] } 44. f4 { [%eval   5.76] [%clk 0:02:19] } 44... Ke7 { [%eval 5.63] 
    [%clk 0:02:30] } 45. b4 { [%eval 5.7] [%clk 0:02:10] } 45... a5 { [%eval    5.93] [%clk 0:02:29] } 46. b5 { [%eval 5.82] [%clk 
    0:02:11] } 46... Kd7 { [%eval 5.89] [%clk 0:02:27] } 47. Rc6 { [%eval 5.    85] [%clk 0:02:11] } 47... Rd6?? { [%eval 9.74] [%clk 
    0:02:20] } 48. Ke5 { [%eval 9.51] [%clk 0:02:11] } 48... Rxc6 { [%eval 9.   77] [%clk 0:02:18] } 49. bxc6+ { [%eval 9.58] [%clk 
    0:02:13] } 49... Kc7 { [%eval 10.01] [%clk 0:02:18] } 50. Ke6 { [%eval 9.   37] [%clk 0:02:15] } 50... Kd8 { [%eval 10.67] [%clk 
    0:02:19] } 51. d6 { [%eval 10.36] [%clk 0:02:14] } 51... b5 { [%eval 10.    49] [%clk 0:02:19] } 52. d7 { [%eval 10.26] [%clk 
    0:02:16] } 52... b4?! { [%eval #2] [%clk 0:02:19] } 53. Kd6 { [%eval #1]    [%clk 0:02:17] } 53... b3 { [%eval #1] [%clk 0:02:20]
    } 54. c7# { [%clk 0:02:17] } 1-0"
}

Dataset Sources [optional]

Lichess: [https://database.lichess.org/]

Dataset Card Contact

This is a sample dataset from the full 53 million game collection.

To access the complete dataset or explore other available datasets, reach out to us at hello@portexai.com.

We’re actively expanding our catalog of high-quality real-world, augmented, and synthetic datasets. If you're a data consumer, we’d love to learn more about your use case and match you directly with the right sources.