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The 'bouncebackability' system


Orpheus

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I saw Vilmoura's thread - 'See Saw Football Betting Strategy' - and there were some similarities with my own, so thought I would post it for comments and public tracking ... Bouncebackability: the ability to be successful again after a period of failure [Macmillan English Dictionary], first coined by Iain Dowie. In other words, this is a system that expects teams to perform better after playing unexpectedly poorly, but also conversely to perform worse after playing unexpectedly well. An earlier version of this system was applied to baseball and outlined here: http://www.punterslounge.com/forum/f21/gambling-shock-baseball-system-85799. That system was too restrictive, but subsequent revisions yielded a very profitable system (proofed on sports-punter.com) until the last two weeks of the season ... in future, I will look to exclude all teams from the system once they cannot reach the post-season playoffs (i.e. the effect on incentives ... do other punters close down their systems as the season draws to a close?). This is a system that should have some applicability to football, but its primary focus is North American sports and especially baseball, basketball and ice hockey as these sports involves teams playing many times per week. Let me outline the four pillars of the system: HomeWin ... if a team wins "unexpectedly" at higher-than-benchmark odds in one game, the next time that it plays and the odds are higher-than-benchmark odds, it will tend not the win the second time. For example, visiting teams will be alerted to how the home team pulled off a surprise result previously and so will amend their tactics to counter this. AwayWin ... exactly the same as the 'home win' element, but just for away games. HomeLoss ...if a team "unexpectedly" fails to win at lower-than-benchmark odds in one game, the next time that it plays and the odds are lower-than-benchmark odds, it will tend to win the second time. For example, losses at home are felt much harder by the club/management/players than away as they lead to much wider criticism in the local press, so will lead to a much stronger effort the next time the situation reoccurs. AwayLoss ... exactly the same as the 'home loss' element, but just for away games. Here's the crucial difference between this system and many others ... the modelling is done on a team-by-team rather than all-team basis. For example, if Man Utd were to fail to win at home, there would always be a very strong expectation that they would react and that the chances of them wining their next home match would increase (in this case, Man Utd's 'HomeLoss' benchmark odds would be very high). Alternatively, if Portsmouth didn't win at home, e.g. they managed a draw, there wouldn't be the same reaction within the club or among the fans to make sure that it wasn't repeated in their next match (in this case, Portsmouth's 'HomeLoss' benchmark odds would be rather low ... only if they failed to win a game against very poor opposition would there be such a reaction). This example shows why the system needs to be team-specific rather than impose some general rules by which all teams are appraised. In terms of basketball and the NFL, I use benchmark spreads rather than odds as there is evidence that coaches use the 'covering the spread' as a measure of how well their team performed relative to expectations. I'm continuing to track this, but I'm not as convinced as with the other sports. I also add filters to limit the plays: (i) when backing a team, they must have shown a profit at the available odds over the sample period. For example, Arsenal are 1.62 against Spurs ... they would be eliminated from the above system as backing them at home at 1.65 or lower over the sample period would have resulted in a loss of 4.86pts (to 1pt stakes). (ii) generally, I only consider first-order lags (e.g. if Man Utd lose twice in a row at home of odds of 2.5 or lower, I will not consider them in the third game that they are 2.5 or lower) unless particular leagues have tendency for higher-order lags to be successful, though this is rare. (iii) I continue to track the system for all leagues, but only post plays if the sum of all plays in that league are in profit for the season (with some follow-on effect from the previous season). This, for example, knocks out leagues which just do not work at all with this system, such as the Belgian Jupiler League. The sample period for each team is either their last 100 home or away games (whichever is relevant) or their home or away games over the last five years (if this produces less than 100 home or away games). As such, the sample period is updated with each game and so the benchmark odds for each team may vary from week to week. Obviously, it is not possible to provide the finer details on a team-by-team basis for such a micro-system, but I hope that the general rules are apparent. I'll post the plays each day that qualify via this system and see if it generates some discussion. In contrast to the plays posted on sports-punter.com, all plays will be graded to the following rules: favourites will be backed to win 100pts; underdogs will be backed with a stake of 100pts. Just one qualifying play tonight. NHL: San Jose (inc OT) 1.60

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