Posts Tagged bets
I did some quick analysis of the market for MLB series prices, comparing three other books to Pinnacle. Because of the volume per bet Pinnacle is willing to take, one can uncover some intriguing insight into what big money bettors might be betting. The other offshore books I used were Bodog, TheGreek, and Heritage, extracting the fair value and calculating the difference from Pinnacle’s listed odds to come up with an overall average market differential. Other than that the tables are self-explanatory, the last column highlights certain teams that may have market value for that series future at the current prices.
It appears Texas has slight World Series market value of a little over 1%, and considerable ALCS value at 3%. They have an interesting draw in the first round against the Rays. Tampa has decided to start the highly touted Matt Moore, who in 9.1 IP this year has 15 K , 3 BB, and a 1.286 WHIP. Moore is a bit of an enigma, a term that can just be thrown around to any player who lacks a sufficient sample size. But the Rays expect tremendous things from Moore. He held his opponents to an OPS under .500 in 52.2 IP while playing for AAA Durham of the International League this year.
The game one line is set at Texas -172 (Wilson) with a total (8 -118/108) right in line with Wilson’s season average. Wilson will pitch again, if necessary, in game four versus David Price, unless Tampa decides to pitch Price in game three. The decision to start Matt Moore means either Niemann or Hellickson (or both) will be moved to the bullpen, at least for this series.
I haven’t found an offshore book that currently has MVP odds posted, unfortunately. The odds above depend on the total number of players receiving votes, so if I limit the odds to only those in the top 10:
Once again, I decided to make an arbitrary formula for pitchers since the distribution of voting points is wildly inconsistent from year to year for pitchers that earned voting points (largely due to the relatively low correlation between voting points and WPA, voting points and ERA or WHIP). In contrast to the NL, where only five pitchers have even been considered for the award since 2000, 51 pitchers in the AL have received voting points over the last eleven years. Unfortunately this does little to satisfy voting trends for pitchers, due to the aforementioned inconsistencies. Because of this, I used the ’99 and ’00 seasons from Pedro Martinez as models for what pitchers have to do relative to offensive players being considered for the MVP award, to finish in the top five. Essentially a 10 WAR pitcher with a WPA around 7 or greater for a playoff team and an ERA+ of about 200 has a legitimate shot to win the MVP in any given season. Justin Verlander falls short of these arbitrary values , and the table above shows where he ranks in the top 15.
We can actually assess how many wins above average Verlander is worth that may offer more clarity. The Tigers score 4.73 runs per game and are 25-9 when Verlander starts. For simplicity, let’s make the assumption that psychological factors do not come into play, and 4.73 r/g is solely contingent on the listed starter of the opposition. When Verlander doesn’t start, the Tigers allow 4.87 r/g. Using Pythagenpat, and an average pitcher resolving Verlander’s 34 starts in the same run environment, the Tigers would win 16-17 of those 34 starts. This would place Verlander at between 8-9 wins above average for his team, and the Tigers would still win the division rather comfortably.
Miguel Cabrera has made a vicious surge in September, with a ridiculous 1.291 OPS and an impressive 2.484 WPA, all this amidst a jaw-dropping .443 BABIP. For the season his BABIP is .363, not outlandish when you consider for his career his hit/contact rate approaches 35%.
Is he the MVP? He’s third in the AL in WAR, and again the table above merely reflects a voting trend for hitters since 2000. But this isn’t 2000. Sabermetrics is an unstoppable force for which there appears to be no barrier. If we rank the contenders solely by WAR, there is still a major flaw. WAR for pitchers and WAR for hitters are founded on different units. Can we convert performance metrics to one robust measure for both pitchers and hitters? Well one can measure runs allowed or runs produced per inning, but hitters account for three or four times as many innings as a typical starting pitcher.
One possible way would be to calculate how many runs the Tigers need to score to maintain that 25-9 record if an average pitcher pitched in place of Verlander. I’m going to use base runs to ensure the units are consistent, and the Tigers allow 4.79 BsR/g when Verlander doesn’t start. The quick way to find the runs needed to maintain a 69% winning percentage over 34 games is to use solver in Microsoft Excel, and the answer is 7.16 BsR/g, which equates to .27 BsR/out. For Cabrera use the BsR formula for offensive players to find an approximate estimation of total run production, and divide by the number of outs (AB – H). The result is .32 r/out. An extremely crude way to compare hitters and pitchers but intuitively Cabrera being worth about .05 more r/out than Verlander is reasonable.
I’m not finished yet. In proportion one can create a scenario where Verlander’s hypothetical offensive output mirrors his pitching output by removing hitters of similar value after a certain number of innings pitched to express innings pitched per start. This scenario was reconciled by the calculations on Verlander in the previous paragraph, but much of the variance can at times be explained by how well the bullpen performs. The goal is for the offense to score 7.16 BsR/g to achieve 25 wins in 34 games. If Verlander averages 7 IP/GS, then after 7 IP his hypothetical offensive performers will be removed from the lineup accordingly, though in this case his equivalent worth will continue on through the 9th inning. The Tigers currently average 4.86 BsR/g during Verlander’s starts, or 1.08 BsR every two innings, which means the Tigers with an offensive player of Verlander’s value inserted into the lineup every inning would score .29 r/out, increasing his runs per out by .02 runs. This explanation at least accounts for a pitcher’s ability to pitch late in games.
I partitioned the pool of MVP candidates into three categories: starting pitchers, relief pitchers, and hitters. That way I can include everybody in the MVP process and isolate the appropriate combination of statistics and coefficients. I’m just trying to resolve as much of the variance in voting points as possible, using numbers from 2000-2010. For the NL Cy Young, initially I used FanGraphs WAR (fWAR) numbers rather than baseball-ref WAR (rWAR). I’ve since decided against it and went back to rWAR. The decision was arbitrary, I might as well have just flipped a coin. The differences are adequately described at fangraphs. If you are out of the loop, search the site for MVP or Cy Young.
Arod’s latest shenanigans and his history of admitted steroid use probably eliminates him from further consideration. The same can be said for David Ortiz and the general stigma behind PEDs.
Jacoby Ellsbury has made headlines the last few nights with walk-off hits, both in relatively big games given the latest surge from the Yankees. Other than a binary appropriation of game-winning hits or his clutch metrics, a flare for the dramatic is hard to quantify. WPA (Win probability added) statistics may be valid here, however. WPA is, essentially, the cumulative WPA for each batting result for a given player. For example, given a win probability for the Red Sox at the time Ellsbury hits a HR or flies out, the change in win probability increases by x or decreases by y. The aggregate sum of each plate appearance (or in the case of pitchers, each pitch) is then presented as a WPA stat. Here is the same MVP table ordered by WPA:
Last year’s MVP, Josh Hamilton, was
second third in the league with a WPA of 6.25. Miguel Cabrera led the majors in 2010 WPA and finished 2nd in the MVP voting. Perhaps I should include WPA into the pool of variables used for regression. While voters probably don’t consciously consider WPA as a tool to assess player value, by definition it is a calculation of one’s contribution to their respective team’s overall success which can as well be approximated through reading recaps and watching highlights.
NL MVP Update tomorrow, will probably try to integrate WPA statistics.
Whether or not playing the market can last for any appreciable amount of time is up for debate. There are many that claim their betting methodology centers around blind market plays. What does that entail? Well first it requires one to have less confidence in having actual knowledge of the sport and more in letting others do the work. As well one should know the market for the respective sport. Whatever that means. Most just assume Pinnacle sets the market, with an often placid interest of the goings on at other sportsbooks. Being a rather placid individual adorned with placid habilimentry, speaking to a placid audience, and fortunate enough to be typing on a placid-looking computer all to better sustain the necessary amount of placidness that is called for to be a placid degenerate, I am perfectly equipped to make wagers with zero knowledge of events I am wagering on.
Eventually this post will demonstrate an extremely simplified version of how to just blindly bet the market, using the Home Run Derby tonight as an example. Step one, assume Pinnacle sets the market price, which consists of the opener, the closer, and everything in between.
Next, calculate the fair-value percentages. Add up the implied probabilities and divide by the total overround.
Pick another sportsbook, preferably one which currently holds a chunk of your hard earned cash from more biblical endeavors. I’ll use BetJamaica.
And the fair-value odds.
I’ve designated pinnacle as the market line, so let’s compare the two through subtraction.
Hopefully this little example is not too much of an insult to market players. One’s ultimate goal in life is to reach a desirable end with little to no exertion, and for the immediate future my ultimate goal is contingent upon the performance of Jose Bautista and Adrian Gonzalez in tonight’s HR Derby. Half unit on each.
*Re-post from last year. Haven’t made any changes to the formula. The example used represents a scheduled game from last season.
I’ve been saying to myself and others, that the exerted effort necessary to complete a worthy and sufficient formula for integrating the variable of the bullpen into creating an MLB game moneyline is not proportional to the degree of change in moneyline. For some reason I was under the impression it wouldn’t make that much of a difference, for most bullpens hover around a 4 ERA (3.5 to 5 at the most), and the ratio of innings pitched to that of a starter in any given game for both teams is such that any projected runs allowed would not see a consequential increased when subjected to the mark of a bullpen.
However, I now realize that I was absurdly wrong on so many levels. After having finally found the proper formula concoction to conduct a bullpen variance, the bullpen adjusted line and the original created line (calculations explained here with changes discussed here), often sees a 40 cent line differential. So let me explain as best I can how I was able to add a satisfactory number to project the impact a team’s bullpen may have on any one particular game.
The created line as it previously stood, was a 162 game projection using ZiPS, CHONE, or actual data, of the starting pitchers’ projected runs allowed of 9IP per game, and a team’s projected runs scored over the season. This gives a nice standard number of runs scored vs runs allowed, used to measure the Pythagorean winning percentage.
How would I add starting pitcher determinant bullpen factor? What I thought best was to take the projected innings pitched by the listed starting pitcher, divided by the number of games projected to start (projections are for now, CHONE, and ZiPS in season projections that is updated regularly). Then use that number as a percentage of 9. Multiply the resulting percentage by the number of runs projected to allow over 162 games, and you get a total number of runs surrendered by that starting pitcher over an entire regular season. Now you still have a certain percentage left over to use as the normalizer of the two dimensional bullpen projection (bullpen ERA * 162) using the YTD statistics as ERA. Multiply this two dimensional projection of the bullpen by what percentage is left over from the starter’s predicted innings pitched per start, and you have bullpen runs allowed over an entire regular season if that particular starting pitcher pitched every game. Add the two numbers, and this should equate to a solid indicator of how a bullpen might regulate the expected team’s pitching performance.
Here is an example of a calculation using Randy Wolf (listed 4/22 vs Nationals):
Projected Runs allowed = Runs / IP * 1460 (allows for randomness)
Runs allowed = (87 / 183.3) * 1460 = 692.96
Adding Bullpen Variable:
IP / GS = IP per game
IP per game = 183.3 / 31 = 5.91
IP per game / 9 = Percentage of IP per game
Percentage of IP per game = 5.91 / 9 = 66%
Projected runs per 162 games = 66% * 692.96 = 457.36
1-Percentage of IP per game * 9= Projected Bullpen IP per Randy Wolf start
1-66% * 9 = 34% * 9 = 3.09 Bullpen IP/g
ERA * 162 = Runs allowed (Flat projection over 162 game season)
Bullpen runs allowed = 5.85 * 162 = 947.7
Runs allowed * Project Bullpen IP per Randy Wolf start
Runs projected per 162 Randy Wolf games started = 947.7 * 33% = 325.07
Randy Wolf projected runs per 162 games + Bullpen Runs projected over 162 Randy Wolf games started
Total expected runs allowed per 162 games = 457.36 + 325.07 = 782.43
782.43 runs allowed is the bullpen adjusted runs per 162 Randy Wolf games started
Let’s compare the numbers, by use of words, and look for proportional reciprocality to ensure a consistent method of calculation.
Randy Wolf’s ERA being 4.27 is considerably lower than Milwaukee’s current bullpen performance, which is a dreadful 5.85 runs per game. The two being separated by about 1.6 runs per game, would leave a reasonable person to assume that Milwaukee’s bullpen will have a negative effect on games started by Randy Wolf. And the resulting numbers show as much.
We projected Randy Wolf to pitch roughly 5.91IP per start. Using this number, his adjusted runs allowed (692.96) is now appropriated to his IP per start, and the result is 457.36. With Milwaukee’s bullpen ERA being considerably higher than Randy Wolf’s ERA, we would now expect the bullpen variant to have a negative impact on runs scored, meaning a consequential increase. Milwaukee’s bullpen has a two dimensional projection value of 947.7 runs allowed per 162 game. And adjusting to Randy Wolf being the listed starter and the number of total runs allowed (782.43) should be higher than the starting pitcher exclusive runs allowed (692.96).
All the variables and determinants appear to line up with rational expectation.
Now using the framework in place, a team with a solid bullpen with an ERA better than that of the listed starting pitcher’s ERA, would correlate to a higher advantage in projected runs allowed if that respective starter is expected to pitch lesser and lesser innings by degree. One problem with that theory, of course, bullpen ERA is not uniformly distributed from reliever to reliever. The more the bullpen is expended, and the earlier it is called on for relief, the more the level of performance regresses to mediocrity.
For now this is the best method I can come up with. If you want the updated excel MLB linemaker, now with a column for starting pitcher predicted line, and bullpen adjusted line, comment here, email or twitter me.