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  • 2024 World Chess Championship analysis: empirical synthesized approach | by Max | Dec, 2024

2024 World Chess Championship analysis: empirical synthesized approach | by Max | Dec, 2024


2024 World Chess Championship analysis: empirical synthesized approach | by Max | Dec, 2024


The 2024 World Chess Championship between Gukesh Dommaraju and Ding Liren captivated chess fans worldexpansive, culminating in an unforgettable finish where Gukesh claimed the title, becoming the youthfulest-ever World Chess Champion.

As a chess enthusiast, I trailed the games shutly — not in genuine time, as the lengthy durations struggleed with my daily responsibilities, but I made certain to watch filled game overwatchs and verify the overall vibrants and momentum. This permited me to appreciate the nuances of the align and get meaningfuler insights into the strategies participateed by both carry outers.

In hindsight, it was an enhappinessable align with plenty of action and momentum shifts, upgrasping me captivated thcimpoliteout. It also igniteed thoughts about carry outer profiles, strategies, and game styles. When the align finished, I had mixed experienceings about the outcome. On the one hand, Gukesh seemed a worthy and well-deserved titenumerate, but I couldn’t help but wonder: was this a fair result? What if Ding hadn’t errored in that last game? What might have happened if the align had transferd to tie-shatters, and how would that have shaped my astonishions of the align overall? How did the carry outers’ carry outances contrast atraverse the align as a whole? These mirrorions led me to verify the align from an empirical and synthesized standpoint, aiming to create a cohesive picture of it as a whole. In this post, I will current my analysis process, split the results, and finish with my final thoughts on the align.

From the timely games of the align, Gukesh seemed to get handle, adchooseing a more active and initiative-driven carry out style. Ding, by contrast, normally ecombineed defensive, at times chooseing for timely draws — even in games where he held the advantage with the white pieces. While this approach permited Ding to stay in the align, it ultimately fchangeed in the final game. In trying to streamline the position and force a draw, he turned an equivalent finishgame into a resolute loss. This contrast in approaches not only depictd the align’s narrative but also shaped its outcome, highweightlessing the dangers and rewards of each carry outer’s strategy.

In graspition, it became evident from both carry outers’ time regulatement that Gukesh ecombineed to be better setd. He was typicassociate quicker during the uncovering phase and upgrasped a time advantage into the midgame, whereas Ding normally set up himself under time prescertain. Ding spent meaningfully more time leanking, even in the uncovering transfers, which may have gived to his difficulties procrastinateedr in the align.

With all that shelp, I reminded myself that Ding only lost due to an cursed error in the last stages of the final game, while he was under time prescertain. Had it not happened, the carry outers would have persistd to tieshatter games with quicker time handles, where anyleang could have happened, and maybe Ding would’ve aascfinishd as the triumphner in this scenario. Perhaps then Gukesh’s active and uncover style could be seen as sloppy and as a sign of inexperience next to Ding’s more conservative and firm style. So perhaps my astonishion of the align was unfair due to the final outcome?

Although many analyses of the align are participateable online, most tfinish to concentrate on a per-game shatterdown, normally diswatching traverse-game synthesis and data-driven insights. I chose to adchoose an empirical, hoenumerateic approach, which grasps using raw game data as measurable evidence — data that is mostly easily accessible — and combining it to originate align-expansive insights from a expansiveer perspective. This approach permits for a more comprehensive empathetic of the align as a whole, rather than isoprocrastinateedd evaluations of individual games.

During my analysis I determined to concentrate on disconnectal (mostly expansively participated) parameters, in an try to either aid or refute the observations made earlier. List of the metrics participated:

  1. Accuracy —A expansively participated metric on online chess platcreates to appraise how shutly a carry outer’s transfers align with the chooseimal transfers recommended by a chess engine. In this metric, engine-originated transfers are pondered chooseimal, and the carry outer’s accuracy score is rerepaird by measuring the degree of deviation from these chooseimal transfers atraverse the game.
    Obtained from Lichess API.
  2. Blunders, Misgets, and Inaccuracies — Another expansively participated metric. It classifies errors based on three categories:
    – Inaccuracy — gentle deviation from chooseimal carry out, usuassociate results in centipawn loss of 0–50
    – Misget — mild deviation from chooseimal carry out, usuassociate results in centipawn loss of 100–200.
    – Blunder — disconnecte deviation from chooseimal carry out, usuassociate results in centipawn loss of >200.
    Obtained from Lichess API.
  3. Average Centipawn Loss (ACPL) — A commonly participated chess metric to appraise the quality of a carry outer’s transfers by measuring their deviations from the chooseimal transfers recommended by a chess engine. In this context, a centipawn recurrents 1/100th of a pawn in material cherish. The drop the ACPL, the shutr the carry outer’s transfers are to engine-recommfinished decisions, and thus, the better their carry out. Unappreciate the previos metric, ACPL recommends a exact numerical cherish for carry outance, making it easier to honestly contrast carry outers. It is particularly advantageous for highweightlessing consistency and minimizing errors over a game or align.
    Obtained from Lichess API.
  4. Move Times —This metric verifys the time carry outers get for their transfers, segmented atraverse games to determine preparation levels and time regulatement trfinishs, particularly in the uncovering phase. A meaningful clock advantage after the uncovering (we see at first ten transfers) can show greater preparation and better time regulatement sfinishs. Additionassociate, this metric provides insight into carry outers’ psychoreasonable states and game momentum, as quicker transfers normally mirror confidence and understandnity with the position. For consistency, the segmentation aggregates all transfers atraverse games, watchless of piece color, combining a carry outer’s first transfers equassociate whether they carry out as White or Balertage.
  5. Conversion Rate — This metric tracks how effectively a carry outer changes triumphning or advantageous positions into actual victories. Calcuprocrastinateedd by dividing the number of triumphs from advantageous positions by the total amount of advantageous positions thcimpoliteout the align. I concentrateed here on advantages of >100 centipawns. In case a carry outer’s advantage drops below 100 centipawns it is counted as a lost advantage.
  6. Comeback Rate — This is the other side of the previous metric, uncomardenting how effective is a carry outer in surviving a didowncastvantage. Calcuprocrastinateedd by dividing the number of comebacks from didowncastvantageous positions by the total amount of didowncastvantageous positions thcimpoliteout the align. I concentrateed here on didowncastvantages of >100 centipawns. In case a carry outer’s didowncastvantage drops below 100 centipawns it is counted as a comeback.
  7. Move Repetitions — This is perhaps the trickiest metric to depict, as it alertages an official standard. The purpose of this metric is to quantify how normally a carry outer trys to repeat a transfer instantly after it was carry outed, typicassociate aiming to accomplish a draw thcimpolite threefgreater repetition. This behavior can be seen as an unofficial draw recommend. While there are cases where repeating transfers is the best or only way to dodge deteriorateing one’s position, such repetitions can discdiswatch uncomardentingful insights about the game. For example, it may highweightless a carry outer’s inclination toward compliant carry out, concentrateing on securing a draw, rather than adchooseing a vibrant or danger-taking approach.

I downloaded the align PGNs from the official world championship website, finish with transfer times. As common — raw data is filthy, so I had to automaticassociate fill in some ignoreing transfer times from the clock readings, and also one PGN was misgetnly uploaded instead of the accurate one, so I accomplished out to the competition set uprs to get this rectified. PGNs are also be participateable at chess.com, albeit without transfer times, fair clock readings.

For the engine analysis data from Lichess (cherishs such as accuracy, mediocre centipawn loss, etc.) I had to manuassociate upload each PGN to Lichess and ask an engine analysis (cursedly this functionality is not exposed via an API at the moment).

After that I could access these analyses via an API and iteratively process them to create a wholesome and cohesive watch on the align.

Next we’ll go over the parameters we depictd and converse them one-by-one.

  1. Accuracy:

The accuracy ecombines to be almost equivalent and the contrastence between the carry outers is negligible. Note this is mediocre accuracy atraverse all games, so although there could be a acunderstandledgeable contrastence when you see at a one game or another, overall it seems both carry outers carry outed on a very aappreciate and high chess level.

2. Blunders, Misgets, and Inaccuracies:

The data on errors, misgets, and inaccuracies shows that Ding carry outed a more exact game, while Gukesh was more prone to errors. However, we must ponder the disconnectity of those errors. Ding made two errors contrastd to Gukesh’s one, and, as we recall, it was that final error that cost Ding the align. This highweightlesss the meaningful role error disconnectity carry outs in chess align outcomes. It seems that while achieving 100% accuracy isn’t always vital, dodgeing critical errors is absolutely vital. We can leank of errors of varying disconnectity as connects in a chain, where the connect strength is equassociate impacted by the error disconnectity and frequency. The more disconnecte and widespread the error — the feebleer the connect. As the saying goes, “A chain is only as strong as its feebleest connect.”

3. Average Centipawn Loss (ACPL):

The mediocre centipawn loss shows a very sweightless advantage (less than 1 centipawn) for Gukesh. This combines well with the accuracy metric we got, which showed a negligible advantage for Gukesh.

4. Move Times:

The transfer times metric aided the earlier observations that Gukesh seemed more setd and more self-guaranteed thcimpoliteout the uncoverings. Ding spent visibly more time during the uncovering on each transfer and naturassociate this accumuprocrastinateeds and results in rather huge contrastences on the clock when the carry outers go in midgame. It would be fascinating to go meaningfuler into this and maybe get insights watching how time prescertain might have caparticipated carry outer’s misgets or errors.

5.6. Conversion and Comeback Rates:

Ding exhibits a evident advantage in both the conversion and comeback metrics, outcarry outing his opponent in both aspects. It’s meaningful to notice that these metrics are inversely roverhappinessed: one carry outer’s comeback rate impacts the other’s conversion rate, and vice versa. This recommends that Ding was not only more effective at capitalizing on his advantages but also discarry outed greater resilience, excelling in situations where survival and recovery were vital.

7. Repeated Moves:

Both carry outers showed virtuassociate the same amount of repeated transfers. I foreseeed Ding would have more repetitions as a mirrorion of his style, and in contrast to Gukesh’s seemingly more vibrant and uncover carry out. However, this assumption was not aided by this metric.

Percreateing an empirical, synthesized analysis of a chess align unaskedly comes with disputes. Chess is inherently vibrant and changeable, where the outcome of individual games can contrast drasticassociate. For instance, a carry outer might carry out insistyly in one game, promiseting multiple errors and losing, but then deinhabitr a firm carry outance in the next game, capitalizing on a individual error from their opponent to triumph. Despite an equivalent scoreline of 1–1, such striumphgs in carry outance can skew overall evaluations, as cumulative misgets from the first game may disproportionately impact seed carry outance.

Another dispute lies in using engines as benchlabels for analysis. While engines provide precious insights, their evaluation methods contrast meaningfully from human reasoning. Engines normally appraise positions sanitizely on objective grounds, diswatching critical human factors appreciate time prescertain, emotional strain, or pragmatic carry outability. An engine might rate a position as equivalent becaparticipate a carry outer in an apparent didowncastvantage has dratriumphg chances — provided they discover a series of perfect transfers. However, this fall shorts to account for the immense difficulty of executing such precision in genuine-world scenarios.

Despite these confineations, empirical analyses can recommend a complementary perspective aextfinishedside other methods, such as commentary from sfinished carry outers or chess bloggers, psychoreasonable evaluations of gamecarry out or behavioral patterns, and personal observations. By synthesizing these contrastent angles, we can create a more nuanced empathetic of the align.

In mirroring on this particular align, the data recommended some astonishing revelations that contrasted with my initial astonishions. Objectively, Ding exhibitd sweightlessly more exact and reliable carry out overall. Yet, his final critical error was resolute, costing him the title. Had the 14-game align finished in a draw, it might have been a fair outcome given Ding’s resilience and steadier carry outance. However, chess, much appreciate professional sports, is unperignoreive; one error can clearurn everyleang. As the saying goes, “One misget can undo a thousand excellent deeds.”

All the resources for this analysis, including the PGNs, the Lichess codes for the engine analyses, the python code participated to verify data and originate plots, and the filled text of this post are uncover sourced in: https://github.com/maxamel/wcc2024

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