We can then easily add the number of unique games and percentage of games that are unique into the unique_games data frame. I’m sure there are also loads of places where my code could be improved. When organizations do this, they will find the ROI they are looking for with their investments in data and analytics. The resulting white_double_rook_sac dataframe only has 100 rows compared to the original dataframe which has over 3.5 million rows, so it’s only once in a blue moon that white sacrifices two rooks this way. Here’s my attempt at that: Then we can call it as follows, to see what percentage of games starting with 1. e4 result in a win for white: … And we get the same 38.94065. There are a couple of things that surprised me here, first being how well the Dutch Defense did for Black, which is a fairly uncommon opening in top level chess that easily leads to unbalanced and unique positions – it outperformed the King’s Indian and the much more conservative Queen’s gambit declined, which actually did quite badly. I find this trend both promising and troubling: promising because organizations are truly wanting to succeed with data, but troubling because they will not find success with the current trends.

Before testing other moves, it’s apparent that to get some meaningful statistics, the win_tendency() and draw_tendency() functions above are still too slow and lacking because they don’t save their result, and only give part of the picture. The graphs for White and Black ratings are basically completely identical, which you’d expect as any player should play both White and Black equally. How might we access for analytics purposes and how to do it at scale? You can import your game in PGN notation or set up a position from a FEN. In the newer cross tables (past few years), it also tracks the color history. When using adply on this function all the data is then added to our df dataframe.

For example “W2\\.” would look up to only the first move by both players, or “W10\\.” would look up until both players have made nine moves. Black wins 32.6% of the time when White plays 1. e4. First thing you might notice is that the V17 column is useless, just containing “###”. Of course, the overall objective and goal is to win, but at times, maybe we bring in different goals and objectives on that path to winning, and a strategy is crucial to success. There’s plenty more data cleaning we can still do. If the data’s right we’d expect it all to be in the format [a-h][1-8], representing the 64 squares on a chess board. For each iteration a new dataframe ndf is generated, the results are put into it and it’s returned. You can also use natural language analysis to get the most human understanding of your game. The Chess Game of Data & Analytics. Eventually, we started to develop strategies to winning. We can also take a look at the squares White’s Queen is most likely to finish on. For registered users we store additional information such as profile data, chess games played, your chess analysis sessions, forum posts, chat and messages, your friends and blocked users, and items and subscriptions you have purchased. To do this we will be writing a web scraper. So far I’ve only analyzed fixed opening moves.

Now (finally!) This page displays every tournament that I have played in and my pre and post ratings.

Earlier I mentioned these functions can be used to lookup a sequence of moves at any point (not just a single starting move), so let’s try plugging in some of the most popular openings beyond just the first move: Here’s what movedf then looks like for these (excuse the bad formatting): Only relevant to chess players: The Sicilian Najdorf is the best opening for Black, scoring an impressive 35% win rate, and a much better choice if the Black player needs to win than something like the Petrov which is much more drawish. It’s also possible to get some stats on individual pieces for each game, such as their last positions and whether they’ve been captured. Beyond rows that have no game moves or an invalid result, we could also easily remove rows that are missing a date (there is a column date_missing that tells us if that’s missing) or if the white or black players rating is missing, or even remove certain rows where the white or black rating was considered too low to be a high quality game. Like with the games where a player lost their Queen and never captured their opponents yet won, if we searched for games where a player finished down significantly in material and won there would be some nice games in there. For this example I’m just tracking Queen moves, firstly because it’s the most powerful piece and secondly because it’s far easier to track when each side only has 1. Those can be removed as follows: If we then run the query to check how many rows have no game moves again, it should come back with 0.

I am finding organizations of all shapes and sizes, industries, etc. $ result : chr “1-0” “1-0” “1-0” “1-0” … $ white_rating : num 2851 2851 2851 2851 2851 …

For example, I may want to pull all games where Black captured a piece on a1 with check, White moved their king somewhere on the second rank, and black then captured a piece on h1 (that would likely be a double rook sac by White). Nf3, 1. c4 and 1. g3), which didn’t surprise me (those all often tend to lead to fairly similar, quieter positions) – nor did the fact that a crazier move like 1. f4 suddenly leads to a far higher loss percentage for white, as it easily results in very unbalanced games especially if it goes 1. f4 e5 (From’s gambit), for example. These cross tables show the pairings and results for every game in a tournament. Now we get into the fun stuff where we can do some analysis on the success of different moves. Looking at the occurrences of each move, the vast majority of games start with a sensible move, which along with the ratings is a decent indication these games are of reasonable quality (if there were a lot of amateur games here I’d expect a lot more 1. a4 and 1. h4).

In my opening blog I outlined that we are going to analyze US chess data to answer some cool questions.

We became stronger players because we had developed strategies to winning, and most importantly, those strategies tied directly back to our goals and objectives.

Let’s turn each column into a suitable data type. “B20.Bf5” – or it could even be multiple moves such as “W1.e4 B1.c5 W2.Nf3” etc) and the second the player colour, being W or B (the function will fail if something else is put in), which determines what player we’re calculating the win percentage for for that move.

This dataset contains over 3.5 million games from high rated players. The USCF website contains the cross tables of every USCF rated tournament. $ number : num 1 2 3 4 5 6 7 8 9 10 … Currently all the columns in our df data frame are just of type character (chr), as can be verified by checking the structure of df as follows: Classes ‘tbl_df’, ‘tbl’ and ‘data.frame’: 3523492 obs. A different group may have its own strategy, but are they talking together? In Bernard Marr’s great book, Data Strategy, he pins down three distinct things data can be used for in a data strategy: These outcomes should help to underpin the data and analytical strategy an organization implements.Along with these three, organizations should also focus on: As organizations look to capitalize on the amazing and valuable asset that is data, they should have a strong data and analytical strategy that ties back to its goals and objectives.

Note though that the moves of the game (at the end of the line) are also separated by a space, and of course we don’t want a separate column for each game move – rather a column containing all the game moves. Welcome to my data analysis blog!

We’ll know that any entries with “na” were games where White had two Queens. Copyright © 1993-2020 QlikTech International AB, All Rights Reserved.

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