# Posts Tagged future

### NL/AL MVP

Sportsbooks haven’t convened MVP odds yet because I haven’t posted them myself.  This is an obvious observation to anybody that visits this blog on a yearly basis.  I think we’d all agree on this.  (I use the terms “we’d all” and “nobody in particular” interchangeably).

The formula behind setting a probability on a given player’s chances can be expressed as:

$P(v_i) = \begin{Bmatrix} \displaystyle \frac{2v_i}{\sum _j^n v_j}, & \mbox {if } v_i>0 \\ 0, & \mbox {if } v_i \leq 0 \end{Bmatrix}$

If a player doesn’t register a positive number of MVP points, the variable v, then he is simply ignored.  The points are calculated slightly differently in the NL and AL, and the years 2000-2010 were used to fit the data.  This has already been explained on multiple occasions.

For AL batters and pitchers:

$V_{ALb} = 57.28(PLAYOFFS) + 12.58(WAR) + 11.08(WPA) + 1.81(HR) + 1212.25(AVG) + 0.58(RBI) - 500$ $V_{ALp} = 50(PLAYOFFS) + 25(WAR) + 15(WPA) - 25(ERA) - 100$

The “PLAYOFFS” variable is either 1 or 0, and in season playoff projections are essentially current standings.

For all NL batters and pitchers:

$V_{NLb} = 78.41(playoffs) + 8.98(WAR) + 10.97(WPA) + 975.41(AVG) + 3.79(HR) + 0.93(RP) + 1.07(SB) - 553$ $V_{NLp} = 30(playoffs) + 10(WPA) + 25(WAR) - 150$

The motivation for using WAR and WPA as primary coefficients stemmed from this post, which I found quite interesting.

At the bottom of the post I’ve attached some relevant excel files.  I’m not going to post anymore about this (I’ll do Cy Young this weekend and attach the necessary files), there really shouldn’t be any reason for me to have to.  I also never want to have to use or look at an excel file ever again.  But if I get enough requests via twitter/email/comments I’ll make a dedicated page that updates daily, probably using my own WAR calculations instead of bRef’s mess of drivel, and some server-side scripting.

Last year the formula picked Ryan Braun and Miguel Cabrera.  Verlander I think can we all agree should not have won the MVP.

NL MVP

 NAME Team bWAR WPA PROB ODDS Andrew McCutchen PIT 5.1 3.2 52.15% -108 Ryan Braun MIL 3.9 3 33.73% 196 Joey Votto CIN 4.5 5.2 33.42% 199 Melky Cabrera SFG 3.8 2.7 18.64% 436 Johnny Cueto CIN 4 2 15.18% 559 Carlos Gonzalez COL 1.6 1.8 8.73% 1045 Carlos Beltran STL 2.3 1.8 6.49% 1441 Matt Holliday STL 3.6 2.8 6.32% 1482 Buster Posey SFG 2.8 1.5 5.44% 1738 Ian Desmond WSN 2.3 3.5 5.41% 1748 Pedro Alvarez PIT 2 1.1 5.29% 1790 Jay Bruce CIN 1.1 0.2 4.13% 2321 Giancarlo Stanton MIA 3 2.5 2.34% 4174 Ryan Vogelsong SFG 2.8 2.1 2.18% 4487 Brandon Phillips CIN 2.2 0.8 0.55% 18082

AL MVP

 NAME TEAM bWAR WPA PROB ODDS Mike Trout TBR 5.3 0.5 34.66% 188 Robinson Cano NYY 5 1.6 31.16% 221 Josh Hamilton TEX 3.2 1.2 22.88% 337 Adrian Beltre TEX 3 1.6 22.13% 352 Mark Trumbo TBR 3.2 0 18.48% 441 Josh Reddick NYY 3.8 4.2 14.96% 568 Alex Rios TEX 2.6 1.7 14.61% 584 Miguel Cabrera DET 3.5 2.2 14.42% 593 David Ortiz BOS 2.7 2.5 6.46% 1448 Matt Harrison TEX 4.1 2.5 5.95% 1581 Fernando Rodney TBR 1.9 2.8 5.87% 1604 Justin Verlander DET 5 3.1 3.51% 2749 Chris Sale CHW 4.7 3 3.18% 3045 Edwin Encarnacion TOR 3 2.5 1.72% 5716

Here are the files. The “NLMVP_ODDS” and “ALMVP_ODDS” files require a data refresh and some sorting.  Feel free to change the coefficients, I don’t care.  Some files may be irrelevant, not sure.  I just threw a bunch of seemingly related files in an archive.

MLB_MVP_FILES.tar

### Comparing Rating Systems For NCAA Tournament

The images represent how each rating system projects the NCAAB Tournament. The higher rated team was selected for each matchup. For the bracket labeled “PINNY”, the future odds from Pinnacle were used to assess team-by-team comparison. Click on the image for full-size view.

### NCAA Tourney KP vs Pinny

Same thing as conference tournaments. SEC Field hit at 3/1 odds, the other four lost. A brief survey of a hypothetical bankroll outcome demonstrated the prodigious and frightening force of the Kelly Criterion and all the emotional turmoil likely to beget its constituency. Flat bettors would have come away in the negative, but with an air of optimism and satisfaction having lingered for hitting a future.

KenPom’s LOG5 predictions are here. If you don’t know what that means, to wit:

LOG5 = (a – a * b)/(a + b – 2 * a * b)

“a” and “b” here are winning percentages. KenPom uses his pythagorean winning percentages calculated by PPP and tempo rather than just points scored for and against, with an exponent of around 12.

(Numbers in each cell represent percentages sans the non-obligatory “%” symbol).

 TOP 5 REGION CHAMP Ohio St 10.54 Ohio St 3.55 Mich St 7.64 Wisconsin 2.24 Wisconsin 6.97 Mich St 1.98 Kansas 6.8 Kansas 1.74 Indiana 3.38 Indiana 0.67

Mr. Pomeroy “likes” the Big Ten, Pinnacle doesn’t.

 SOUTH KP PINNY KP-P TEAM REGION CHAMP REGION CHAMP REGION CHAMP Kentucky 47.9 19.7 47.4 27.78 0.5 -8.08 Wichita St 11.8 2.6 8.43 2.32 3.37 0.28 Indiana 9.2 1.7 5.82 1.03 3.38 0.67 Baylor 10.9 1.7 12.08 2.82 -1.18 -1.12 Duke 9.5 1.7 12.08 4.8 -2.58 -3.1 UNLV 3 0.2 3.51 0.73 -0.51 -0.53 Iowa St. 1.7 0.1 1.31 0.42 0.39 -0.32 Notre Dame 1.9 0.1 1.96 0.44 -0.06 -0.34 Uconn 0.9 0.06 2.58 1.07 -1.68 -1.01 Xavier 0.09 0.04 1.32 0.43 -1.23 -0.39 S Dakota St. 0.8 0.03 0.41 0.29 0.39 -0.26 VCU 0.5 0.02 0.79 0.29 -0.29 -0.27 Colorado 0.4 0.01 0.67 0.29 -0.27 -0.28 NMSU 0.3 0.01 0.41 0.35 -0.11 -0.34 Lehigh 0.3 0.007 0.4 0.21 -0.1 -0.203 WKY 0.001 0.82 0.32 -0.819 -0.32 MIDWEST KP PINNY KP-P TEAM REGION CHAMP REGION CHAMP REGION CHAMP UNC 28.5 6.6 32.95 13.64 -4.45 -7.04 Kansas 33.7 9.1 26.9 7.36 6.8 1.74 Gtown 9.7 1.4 7.31 1.45 2.39 -0.05 Michigan 5.7 0.5 4.57 0.88 1.13 -0.38 Temple 2.3 0.1 3.92 0.64 -1.62 -0.54 SDSU 0.9 0.03 2.65 0.52 -1.75 -0.49 St. Mary’s 1.2 0.05 2.65 0.59 -1.45 -0.54 Creighton 2 0.1 1.61 0.43 0.39 -0.33 Alabama 3.1 0.2 2.04 0.57 1.06 -0.37 Purdue 3.9 0.3 3.92 0.73 -0.02 -0.43 NC State 1.5 0.07 4.57 0.73 -3.07 -0.66 USF 0.3 0.008 0.81 0.66 -0.51 -0.652 Ohio 0.5 0.01 0.81 0.29 -0.31 -0.28 Belmont 4 0.03 3.92 0.85 0.08 -0.82 Detroit 0.07 0.54 0.21 -0.47 -0.21 Vermont 0.03 0.84 0.39 -0.81 -0.39 WEST KP PINNY KP-P TEAM REGION CHAMP REGION CHAMP REGION CHAMP Mich St 35.2 12.4 27.56 10.42 7.64 1.98 Missouri 23.1 5.3 22.63 8.31 0.47 -3.01 Memphis 8.2 1.7 5.67 1.61 2.53 0.09 New Mexico 7.1 1 7.84 1.33 -0.74 -0.33 Marquette 7.5 0.9 9 2.34 -1.5 -1.44 Loserville 4.7 0.5 9.08 2.61 -4.38 -2.11 Florida 4.4 0.5 3.97 0.8 0.43 -0.3 St. Louis 3.4 0.5 2.2 0.57 1.2 -0.07 Virginia 2.5 0.2 1.78 0.43 0.72 -0.23 Murray St. 1.4 0.07 3.05 0.73 -1.65 -0.66 LBSU 1 0.06 1.3 0.29 -0.3 -0.23 BYU 0.5 0.02 3.91 0.97 -3.41 -0.95 Davidson 0.3 0.009 0.71 0.29 -0.41 -0.281 Colorado St. 0.4 0.008 0.52 0.29 -0.12 -0.282 LIU 0.003 0.39 0.17 -0.387 -0.17 Norfolk St 0.0001 0.39 0.21 -0.3899 -0.21 EAST KP PINNY KP-P TEAM REGION CHAMP REGION CHAMP REGION CHAMP Syracuse 17.5 4.4 18.22 5.72 -0.72 -1.32 Ohio St 45.9 19.3 35.36 15.75 10.54 3.55 FSU 3.9 0.5 9.29 4.08 -5.39 -3.58 Wisconsin 16.2 4.2 9.23 1.96 6.97 2.24 Vanderbilt 4.9 0.8 7.92 2.81 -3.02 -2.01 Cincinnati 1.8 0.2 4.39 1.03 -2.59 -0.83 Gonzaga 1.7 0.1 2.4 0.59 -0.7 -0.49 Kansas St 3.4 0.4 4.39 0.98 -0.99 -0.58 S. Miss 0.2 0.006 0.98 0.34 -0.78 -0.334 WVU 0.8 0.05 2.4 0.59 -1.6 -0.54 Texas 2.3 0.2 2.2 0.52 0.1 -0.32 Harvard 0.7 0.04 1.11 0.29 -0.41 -0.25 Montana 0.09 0.002 0.79 0.29 -0.7 -0.288 St. Bona 0.6 0.03 0.53 0.29 0.07 -0.26 Loyola 0.02 0.4 0.17 -0.38 -0.17 UNC-Ashe 0.03 0.4 0.17 -0.37 -0.17

### MLB Playoff Market

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.

### AL MVP Update and BsR

 NAME TEAM rWAR WPA PROB ODDS Miguel Cabrera DET 7.00 7.60 22.38% 347 Jacoby Ellsbury BOS 7.30 6.00 19.03% 425 Adrian Gonzalez BOS 6.70 3.70 17.69% 465 Jose Bautista TOR 8.60 8.20 17.10% 485 Robinson Cano NYY 4.80 3.00 11.09% 802 Justin Verlander DET 8.50 4.90 9.68% 933 Curtis Granderson NYY 5.30 3.20 9.46% 957 Josh Hamilton TEX 3.60 4.90 9.18% 989 Dustin Pedroia BOS 6.50 2.00 8.86% 1028 Adrian Beltre TEX 5.20 1.50 8.29% 1106 Michael Young TEX 2.40 2.80 8.16% 1126 David Ortiz BOS 3.80 2.00 8.14% 1128 Mike Napoli TEX 5.00 1.30 7.65% 1207 Alex Avila DET 5.60 2.90 7.54% 1226 Victor Martinez DET 2.70 3.20 7.33% 1264

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:

 NAME TEAM rWAR WPA PROB ODDS Miguel Cabrera DET 7.00 7.60 33.72% 197 Jacoby Ellsbury BOS 7.30 6.00 28.67% 249 Adrian Gonzalez BOS 6.70 3.70 26.65% 275 Jose Bautista TOR 8.60 8.20 25.76% 288 Robinson Cano NYY 4.80 3.00 16.70% 499 Justin Verlander DET 8.50 4.90 14.58% 586 Curtis Granderson NYY 5.30 3.20 14.25% 602 Josh Hamilton TEX 3.60 4.90 13.84% 623 Dustin Pedroia BOS 6.50 2.00 13.35% 649 Adrian Beltre TEX 5.20 1.50 12.49% 701

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 runsThis explanation at least accounts for a pitcher’s ability to pitch late in games.