Absolute backgammon6/30/2023 ![]() ![]() I keep my on-line profile, where I play matches against real opponents, separate from any practice I do against XG-Gammon in tutor mode, so when I do the above steps I get the genuine record of how well I’ve played. Actually, this is me being snobbish – any backgammon players reading this who don’t want to use specialist analytical software like R, you can do a lot with Excel and I encourage you to have a go. save it as a CSV, close Excel and load the data into a real analytical environment like R.open Excel or equivalent and paste it in.“sessions list” (not sessions list (Match Play) – for me, this just freezes my system).From the “Results” menu, choose “Copy to clipboard”.Against humans, I play mostly matches (eg first to X points, where common values of X are 3, 5, 7 and 11) and in this post I’m analysing match-level data that is, ultimately I am using a spreadsheet generated by XG-Gammon where there is one row for each match I’ve played, with 54 columns of interesting data about that match. If the user saves the analytical results to player profiles, you build up a database of games and matches. Online sites like FIBS (the First Internet Backgammon Server – open, free for all, lots of swearing, a bit clunky but a a great environment) and grid.gammon (invitation only, more players and more of them are very serious and skilled) let you save games you’ve just played in formats that can be opened and analysed for luck and mistakes by the various backgammon-playing software. We quantify our errors in post-match analysis (you can play a computer in tutor mode too, and that’s the best way to train, but not the subject of today’s post).Īs well as being the best backgammon players on the planet, XG-Gammon and other software such as GNU Backgammon and Snowie are used by many backgammon players to analyse their own matches, to identify areas for improvement and (let’s face it) to find out if the opponent really was as lucky as we thought they were. Of course, while you’re playing a human under match conditions, you don’t know that you just gave up (for example) 0.096 of your equity, although sometimes you have a pretty good idea from a nagging feeling of “I really shouldn’t have done that…”. Here’s a post from ancient history (ie 2005) on the origin of the term “blunder” in backgammon – apparently it only goes back to 1998, who knew? ![]() Whereas gaining 0.300 of equity by a luck dice roll is a common threshold for a “joker”. If you give up 1, you just moved from being certain to win to certain to lose in practice losing 0.080 of your equity in one turn is usually defined as a “blunder”, and 0.020 as an “error”. Each decision by a player can be compared with an optimal play that the computer would have made, and the computer can estimate exactly how much equity in the final result you just gave up by playing differently to how it would have done. Since the rise of robot players that used machine learning neural networks to identify winning strategies, we’ve had a new way to quantify exactly how much skill and how much chance. For a complete mismatch – a master playing a beginner – the master will win about 75% of one point matches, and nearly 100% of matches to 11 or more (the longer the match, the more chance for skill to emerge as dominant) – see my earlier post on those odds. Consider that for two exactly equally skilled players, the result appears to be 100% luck and the best forecast for a result is a coin flip. Backgammon is a game that combines chance and skill, and everyone who comes across it asks “how much is luck, and how much skill?”. ![]()
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