I use in-season data to formulate my baseball picks so just about ready to start the season in a couple days. First, I'll give an idea of what to expect based on last season's results of the same work in case anyone is influenced by the selections.
I have a detailed database from 2011 of my formulas which allows me to continuously tweak the matchup margins and money line limits to see where the best value is/was. Because of the ability to post-tweak, the results are the best possible if I knew where to set the limits beforehand. In other words, backfitted.
Before anyone dismisses the backfitted data, know this: the formulas were already solidly profitable in 2011 in real time throughout the season. It was during the offseason that I backfitted the results to improve the output further. This is necessary when you're constantly accumulating data season to season. It takes thousands of games to fully allow averages to round into shape.
I developed this formulaic concept a couple years ago after many years of trial and error and applied it to 1,816 MLB games last year starting on May 17th and going right up to the final day of the regular season on September 29th. I have several different formulas that look at a game from many different angles. Roughly a third of the games meet a certain conflict within the formulas and are eliminated automatically. If a game has no conflicts, it becomes a wager. Of the 1,816 games last season, 1,254 of them met no conflicts. Wagering on every one of these games without any kind of requirements (simpy the team with any edge at any money line, including -300 or higher) was 695-559 (55.4%) +41.33 net units at 1 unit per wager. I used tentative requirements for margins between the two teams and no favorites higher than -130 for my actual wagers last year so my results were better but I just wanted to share the bare-boned results with no strengthening first to show the foundation was strong.
By tweaking the matchup margins and money line requirements in the offseason, I was able to set the money line limits to -180 for home teams and -160 for road teams. Seems high, but since I eliminate the conflicted games I'm left with stronger situations which offsets the higher prices. This also allowed me to make the margin requirements stricter to protect against the higher favorites. This is a true "system" in that all the components work together to hopefully strengthen the final plays. After doing all of this, the optimal final results were tweaked to 497-345 (59.0%) +134.24 net units in 132 days at 1 unit risk every game. I define a "unit" as the standard amount you would wager per game. If you wager $100 per game, this would be $13,424. At $1000 per game, $134,240. I don't expect a unit per day this year because those were optimal, backfitted results but I do expect a certain level of success.
Stats:
-Favorites: 366-194 (65.4%) +117.12 units; Underdogs: 131-151 (46.5%) +17.12 units
-6.38 plays per day; will have action on 67% of the schedule
-The average price of all 842 final plays last year was -120. I try to wager "to win" a unit because I find the ROI to be higher that way; so what I do is subtract the average price from my set wager amount (always wager the same amount on each game). If I'm wagering $100 per game in a -110 sport like basketball, I would adjust that in baseball by subtracting the average price (-120, so I would subtract around 20%) and make my wagers "to win" 80. Underdogs would be risking $80 as well. Also I would keep your wagers 1-2% of your available capital.
-At an average price of -120, a winning percentage of 54.55% or higher is required for profit. Mine was 59.03% optimally, so there's room for error.
-Plays will start on Monday
I have a detailed database from 2011 of my formulas which allows me to continuously tweak the matchup margins and money line limits to see where the best value is/was. Because of the ability to post-tweak, the results are the best possible if I knew where to set the limits beforehand. In other words, backfitted.
Before anyone dismisses the backfitted data, know this: the formulas were already solidly profitable in 2011 in real time throughout the season. It was during the offseason that I backfitted the results to improve the output further. This is necessary when you're constantly accumulating data season to season. It takes thousands of games to fully allow averages to round into shape.
I developed this formulaic concept a couple years ago after many years of trial and error and applied it to 1,816 MLB games last year starting on May 17th and going right up to the final day of the regular season on September 29th. I have several different formulas that look at a game from many different angles. Roughly a third of the games meet a certain conflict within the formulas and are eliminated automatically. If a game has no conflicts, it becomes a wager. Of the 1,816 games last season, 1,254 of them met no conflicts. Wagering on every one of these games without any kind of requirements (simpy the team with any edge at any money line, including -300 or higher) was 695-559 (55.4%) +41.33 net units at 1 unit per wager. I used tentative requirements for margins between the two teams and no favorites higher than -130 for my actual wagers last year so my results were better but I just wanted to share the bare-boned results with no strengthening first to show the foundation was strong.
By tweaking the matchup margins and money line requirements in the offseason, I was able to set the money line limits to -180 for home teams and -160 for road teams. Seems high, but since I eliminate the conflicted games I'm left with stronger situations which offsets the higher prices. This also allowed me to make the margin requirements stricter to protect against the higher favorites. This is a true "system" in that all the components work together to hopefully strengthen the final plays. After doing all of this, the optimal final results were tweaked to 497-345 (59.0%) +134.24 net units in 132 days at 1 unit risk every game. I define a "unit" as the standard amount you would wager per game. If you wager $100 per game, this would be $13,424. At $1000 per game, $134,240. I don't expect a unit per day this year because those were optimal, backfitted results but I do expect a certain level of success.
Stats:
-Favorites: 366-194 (65.4%) +117.12 units; Underdogs: 131-151 (46.5%) +17.12 units
-6.38 plays per day; will have action on 67% of the schedule
-The average price of all 842 final plays last year was -120. I try to wager "to win" a unit because I find the ROI to be higher that way; so what I do is subtract the average price from my set wager amount (always wager the same amount on each game). If I'm wagering $100 per game in a -110 sport like basketball, I would adjust that in baseball by subtracting the average price (-120, so I would subtract around 20%) and make my wagers "to win" 80. Underdogs would be risking $80 as well. Also I would keep your wagers 1-2% of your available capital.
-At an average price of -120, a winning percentage of 54.55% or higher is required for profit. Mine was 59.03% optimally, so there's room for error.
-Plays will start on Monday
