Hello everybody. Peabody here.
Shame I can’t earn any endorsements for the upcoming movie, because I can so do that voice. At least the original one, and when I’m not cold-ridden like I’ve been this weekend, pretty much confining myself to the room with the wood stove.
So I have looked back at the projections I released two weeks ago, and I did find one major mistake. Yes,there was much criticism of my methods being extremely conservative and not deviating very far from average – criticism which I didn’t necessarily take at full value, because, well, it is generally true. The methods, and the decisions leading to those methods – things like forcing the league totals to conform to last season’s league totals – force the system into a conservative mode. My default assumption is that there was nothing out of the ordinary.
But when I ran followup tests, like the average error of forecast components from the last few years – I found that I was going seriously astray. The process was something like this:
a) run analysis of the player’s performance over the last three years to set a baseline of expected performance. That is essentially just a weighted average of the last 3 seasons, with weights that vary by stat – some are more sensitive to just the most recent season, some to the entire three-year average, and some have little predictability at all.
b) compare that baseline performance with the baselines of players from baseball history. try to see if there is a consistent deviation from those baselines that can be applied to the current player.
Now, the weights in step A could be something like .523, .233, .150, which are for the hitter’s strikeout component. You’ll notice that they only add up to .896. That difference between the sum opf the components and 1 is a measure, a recognition, of regression to the mean – partiuclarly since my components are zero-based to league average. For a highly predictive statistic like batter K, the sum is close to 1. For hitter batting average, the sum is only .620; for pitcher delta-runs, it is barely 0.2.
The second step goes something like Baseline+delta*x, where delta is the difference between comparison players and their baselines, and x is an indicator of how useful those adjustments are. They go as high as 1, for speed and power, and are pretty near zero for things like those pitcher delta-runs.
The trouble is that I calculated the x component in a way that repeated the regression to the mean, essentially (baseline+delta)*x. The RTM was being double counted.
For an average player, the difference was essentially meaningless. But the more extreme they were, in any facet that I measured, then the bigger the effect. So Mike Trout, above average across the board, went from
BA OBA SLG EQA EQR WARP cPOW cSPD cSO cBB cBA Mike Trout 0.302 0.386 0.510 .316 110 7.3 11 4 0 5 12 0.306 0.406 0.530 .332 123 8.9 12 7 -1 8 15
(these are from the ‘all hitters’ section, straight from the computer, without regressing to league norms; the numbers on the projection pages will be a little lower).
Speed was dramatically affected, in part because the most extreme players are so much farther from the average. Trout went from 26 SB to 40; Billy Hamilton went from 43 to 72. His power dropped from a -9 component before to -11 now (sometimes the R-T-M works in your favor). Miguel Cabrera went from 30 HR to 36.
Fortunately, the pitchers weren’t similarly affected; the double-counting coding error didn’t happen in that directory. I did take advantage of my analysis to updte the weights, which made for some differences. And the overdone regression to means had infected the fielding analysis as well, so that teams with good fielding weren’t getting enough credit for it, which did feed back on the pitcher ratings.
The effect on teams was dependent on having extreme players. Those that did, benefitted by perhaps a win, maybe two. It did let a little more spread into the standings, with peak wins inching up from 91 to 93 and min wins dropping from 67 to to 66.
So a quick look at the changes on the team level since 1/24, not all of which come from my code changes:
AL East: was TB 90 Bos 86 NY 85 Tor 78 Bal 77 now 90 89 86 79 81
The Orioles’ gain is mostly from me jumping the gun and sending A.J. Burnett their way, as he represents a big upgrade over their assorted fifth starter contenders. There was also a component for opponent quality that wasn’t kicking in – while the teams in the AL East were being judged harshly because of their ferocious schedule (playing other AL East teams), they weren’t receiving the compensating break – that their record isn’t an unbiased assessment of quality when they are NOT playing in the AL East.
AL Central: was DET 91 Cle 85 CWS 79 KC 77 Min 72 Det 89 82 78 77 71
And that quality change I just spoke kicks the AL Central in the teeth. Kansas City does well to stand pat with their 77-win forecast – the addition of Bruce Chen helps a little – while everyone else drops 1-3 games.
AL West: was OAK 88 TEX 87 LAA 84 Sea 83 Hou 70 now 91 85 86 81 67
Fixing the RTMs hurt Houston. Seattle was especially hurt by the changes in fielding, as they will have a lot of positional uncertainty – even the presumptive addition of an overrated Nelson Cruz doesn’t save them from a drop. Hosuton was also hurt by that, but Oakland did just fine. Trout alone benefitted by 15 runs from fixing the RTM error, and the Angels gained two games.
NL East: was WAS 87 ATL 85 NY 78 Mia 75 PHI 72 now 88 84 77 73 73
The Nationals gain a game on the Braves, based on the changes I made, because I don’t believe there’s been any player movement outside the bullpens.
NL Central: was STL 90 PIT 83 CIN 80 MIL 77 CHC 67 now 93 83 78 80 66
The Brewers added Matt Garza and Francisco Rodriguez, both pretty nice pickups, and Mark Reynolds makes their first base situation a little less desperate…but I am surprised at how they’ve switched places with the Reds. I promise, I’m not making any deliberate moves to hold the Reds back, but they keep on slipping.
NL West: was LA 88 SF 85 SD 83 Ari 78 Col 71 89 85 81 78 72
Not much change here, with the most notable one being the Padres’ loss of Luebke for the season. I don’t see Arroyo doing much but adding depth – he’s no better than the mostly Randall Delgado innings he replaces – and ditto for Maholm and the Dodgers.
Davis’ component-power score is only projected to be a +35 (he says “only”), after putting up a surprising 50 last year.
His three-year line for power going into 2014 is 29, 27, 50. I did a quick search for players who
1) had 250 PA each across a four season span
2) were 26-30 in the fourth season (Davis will be 28 this year);
3) averaged at least a +15 power in the first two years
4) was at least 15 runs better than each of the first two years
That gave us this list:
yr4 age4 pow1 pow2 pow3 pow4 pow4-3 Jack Cust 2008 29 9 26 43 41 -2 Juan Diaz 2001 27 20 12 39 31 -8 Jim Gentile 1962 28 19 26 51 25 -26 Willie Horton 1969 26 22 26 41 21 -20 Todd Hundley 1997 28 19 20 43 36 -7 Adam LaRoche 2007 27 17 15 33 14 -19 Joey Meyer 1988 26 22 21 43 17 -26 Jai Miller 2012 27 20 19 38 16 -22 Kevin Mitchell 1990 28 16 18 54 32 -22 Mike Napoli 2009 27 22 15 39 19 -20 Dave Nicholson 1969 29 19 17 37 20 -17 Gene Oliver 1962 27 20 14 35 10 -25 David Ortiz 2004 28 17 17 32 34 2 Carlos Pena 2008 30 24 14 52 31 -21 Mark Reynolds 2010 26 20 22 39 34 -5 Tony Solaita 1976 29 22 19 54 15 -39 Gorman Thomas 1979 28 22 28 48 55 7 Jason Thompson 1982 27 12 21 42 30 -12 Jim Wynn 1968 26 15 16 35 29 -6
Only 2 out of 19 players (David Ortiz and Gorman Thomas) managed to up their power score yet again in the fourth year. The mean change from year3 is -15; the media change is -20. After averaging 42 in the boom year, they averaged a (still repectable, and still better than the first two years) 27 in the fourth year.
Show your projections from last year.
On the Projections page, there are links to the 2012 and 2013. They are from the saved spreadsheets that I have from the dates given, and run through the same csv-to-webpage script I used to make the current pages.
To help people understand how the #1 team is forecasted to “average” 91 wins, can you also show the averages for #1 through #30? That is, take the highest win total for each of your simulations (regardless of team), and show us that average. Then do the same for the second highest and so on.
He has no team winning more than 91 games… very likely.. lol
Tom Sheffield says:
It’s still way too early for projections like this but I do find great fault with 91 wins being the best record in baseball this year. The AL East looks about right standings wise.
There’s an issue here that I find hard to explain.
It is almost certainly NOT the case that the best record in baseball will only amount to 91 wins. In fact, if you looked at the playoff chances page, you’ll see that the AL East says this
Average wins by position in AL East: 95.2 87.7 81.9 76.1 68.5
indicating that it will take 95 wins, on average, to win the division – even though no team in the division, on average, gets above 90. Every division, in fact, takes 94-95 wins to finish first. WTF? Teams don’t win _on average_. The winning team will be the one who combines a good projection AND beats their projection. If the past three years are any indication, the average team is going to be 5 games off these projections – and a couple of teams will miss by 20. In the odds page, I play the season out a million times. In the real world, it will only play once, and how you perform relative to your projection determines your final standing.
There is no doubt in my mind that the best teams will be better than their projection, and the worst teams will be worse. Last year, the six first place teams averaged 8.7 wins better than their projection. Only the Tigers were able to underperform their projection and still win their division.
The six second place teams were +6.5.
The third place teams averaged -0.2…basically zero. Just meeting your projection is a recipe for mediocrity.
The fourth place teams averaged -3.
The last place teams averaged -10.
Whether the projection error comes from mis-estimating the real quality, or just random luck, or a mid-season tradeoff of talent from the weak to the strong that exaggerates the difference…there will be errors, and they have as much to do with deciding the winners as real talent. I’m sorry if that sounds like a copout.
David Lowe says:
You might want to tweak your software. The Royals aren’t going to be 9 games worse than they were last year, bro.
Insane! How does the computer project the Royals to get worse??? With that defense and relief corps? No way…
Any projection is going to upset fans of various teams, especially if the projection comes in lower than they think is deserved.
With the Royals, the big concern for me is the pitching. I expect Shields to come back about a half run in ERA, and I don’t see quality replacements for Santana and Chen, who surprisingly put up over 400 IP @ 3.50 ERA. Two things I will concede – there is some evidence, looking at the last two years of projections, that I under-count defense…or rather, that teams with good(bad) defense don’t get their runs allowed moved down(up) enough. The Royals and Orioles are two teams who might be suffering from that bias…if it is real. It didn’t show up in the 2011 data with nearly the same effect as in 2012-13.
Now, Guthrie at a 5.00-ish ERA. I’m perfectly comfortable with that projection. He was 20 runs above average in the DR component – my way of saying he gave up 20 runs less than expected, base don his other stats. He doesn’t have a history of putting up that kind of number, and even if he did, that component score heavily, heavily trends towards zero in future years. The issue I have with the projection, in retrospect, is that there’s no way he gets 30 starts with that levelof performance. Its not as though there’s a ton of depth there, though, so its not going to make a big difference, but future iterations are liable to come up a a couple of wins for them. It IS a process to run these stats, and this was just an opener.
Sorry, but if you think the Reds will be under .500 your computer has a bad virus.
I predict that Cincinnati fans will become thoroughly sick of the phrase “you can’t steal first base” this season.
My first run (that I’m willing to talk about) of projections for the coming season is now up on the 2014 Projected Standings tab. They have also been used to create a new Playoff Chances Report. And, of course, the individual projections that go into are available, again on the Projected Standings page.
|East||Won||Lost||Runs||Runs A||Champ||Wild Card||Net Playoff|
|Central||Won||Lost||Runs||Runs A||Champ||Wild Card||Net Playoff|
|West||Won||Lost||Runs||Runs A||Champ||Wild Card||Net Playoff|
|East||Won||Lost||Runs||Runs A||Champ||Wild Card||Net Playoff|
|Central||Won||Lost||Runs||Runs A||Champ||Wild Card||Net Playoff|
|West||Won||Lost||Runs||Runs A||Champ||Wild Card||Net Playoff|
To build these projections, I:
1) Run a computerized projection scheme, using the last three years of player performance compared against a database of all players’ four year performances. The algorithm attempts to find the most similar players, in terms of age, position, build, and performance, and the top 20 players are noted on the individual player cards.
2) Take those performances, and enter them into a very large spreadsheet, where I fill in expected playing times for all of the players. Every team, every position has to equal 100%. There have to be 162 pitching starts. Generally speaking, a) no position player gets more than 90%, and pitchers are mostly capped at 32 starts; b) rookie starters don’t get more than 80%; c) players I don’t think can hold the job all year certainly get less; d) the playing time estimates from the computer tend to carry a lot of weight. I normally set a sure starter to the 5% playing time level that first passes their projected PA, while innings are usually held under the computer’s values.
All of the statistics in the spreadsheet get rebalanced and weighted. Players on teams with high OBAs will get more plate appearances. Defense trickles back into pitchers hits (and runs) allowed. The league as a whole has to come out equal to the league totals of last year.
Current free agents won’t show up here – no team, no projected playing time. Their projections are still available on the “All hitters” and “All pitchers” downloads.
Getting to some of the players takes a deep depth chart. I’ve prepared some that you can find under the 2014 Spring tab, under “dts”. Every team has three files in there. One is a dt file, which contains the translated statistics, 2009-13, with the computer-only 2014 projection, for all hitters in that team’s system; another is a pdt file, which does the same for pitchers. The “orgdt” file just has the 2014 projections for all players on the team, sorted by position and projected WARP, like the one here for the Nationals. Kind of works as a very deep depth chart for all teams, although I can’t swear that aren’t players showing up on the wrong team (especially for players who have been released – there’s a decent chance they still show up for their old teams). That’s just for these depth charts – I am reasonably certain that every player used in the major league projections is actually a member of their team. The one exception might be Matt Garza, who I have already written into the Milwaukee rotation.
Looking back on the Hall of Fame issues that came up, I think quite a few of the problems would disappear if they would just have a real election.
What, you say they already have one? No, they do not. Maybe I’m being overly pedantic, but an election, to me, is a way of choosing people to fill a position that must be filled. In particular, it has to result in a winner. The Hall of Fame selection process does not ensure a winner; it is more akin to the process of passing a piece of legislation than to the process of selecting a legislator.
The Baseball Hall of Fame has a pretty basic conflict. The Hall itself – and the community that founded it – desires, and needs, to have induction ceremonies held every July, and induction ceremonies without inductees is just bad for business. This argues for making voting easier, to ensure that we don’t have another repeat of 2013, when no one was selected.
On the other hand, they have given the keys of election to a group – the BBWAA – which seemingly takes more pride in denying entrance to the unworthy than welcoming the worthy. The procedures they have adopted also are intended to exclude all but the best.
Looking at things from a large, historical perspective, we see that major league baseball recognizes 2425 team-seasons in major league history – 1256 in the NL, 1048 in the AL, 85 in the 19th century American Association, 16 in the Federal League, 12 in the Union Association, and 8 in the Player’s League. Personally, I’d include all the teams in the National Association of 1871-75 as well, which would bump us up another 50, getting us to 2475.
There have also been 211 players elected to the Hall of Fame – not counting managers and Negro League players. I’d also include a few players from the NA days who were inducted as “pioneers”, but whose playing career demonstrates at least some worthiness (George Wright and Al Spalding for sure; Candy Cummings is more questionable). I’d also add to the list of players some obvious selections (based on their play) who have been denied entrance for moral failings of one kind or another – let us say Joe Jackson, Pete Rose, Mark McGwire, Barry Bonds, Sammy Sosa, and Roger Clemens. That is 220 players, 2475 teams, or a player for every 11.25 teams in history.
That was the most expansive definition. If I wanted to be stricter, I could just look at the 211 players selected to the Hall. And I could throw out the NA teams, and all the third leagues, and probably the first three years of the AA, when it’s quality level was way, way below the NL of the day. That produces a narrower list of 2358 teams. Ratios vary from 10.72 (using the largest number of players and smallest number of teams) to 11.73 (the reverse). To be less precise – there’s been a Hall of Fame player selected for every 11 or 12 teams in history.
Since there are currently 30 teams playing in the majors every year, it means that if you simply accepted the existing ratio as a guide, then we should be creating around 2.5 new Hall of Famers every year just to keep up.
So my proposal to the Hall of Fame committee is this – make it a real election. The top vote getter each year gets in, regardless of the vote count. The second-place finisher gets in, assuming a 50%+1 approval. The third (or more) person goes in if they can pull a 75% approval.
In all of Hall voting, there have only been two players who have finished first or second in the voting without currently being in the Hall of Fame – Craig Biggio (1st in 2013, and near-certain to crack the threshold at some later date) and Jack Morris (who finished 2nd in 2013). Even in third, there’s only a few cases – Jeff Bagwell in 2012-13, Tony Oliva in 1988, and Gil Hodges four times in the 70s. I don’t think the Hall would be in any way diminished by these inclusions.
How would the last 25 years elections have worked following my rules? I’m going to make the naive assumption that votes for other players would not have changed due to players that I’ve removed from the ballot by inducting them before their time.
1994 – The real Hall tabs Steve Carlton, and we concur. But we will also honor Orlando Cepeda, who picked up 73.5% while finishing second, and won’t make him wait until a Veterans Committee meeting in 1999.
1996 – No one is elected by the real Hall. Niekro was first, so we skip him; that makes Tony Perez #1, so in he goes without waiting four more years. Sutton is next, skip him, and that brings up Steve Garvey…but he only has 37% vote. Perez is our only inductee this year.
1997 – The real Hall chose Niekro, followed by Sutton and Perez, all of whom we’ve already honored. The top recipient, and our winner, even though he only had 39% of the vote, is Ron Santo. We salute him in 1997, instead of making him wait until the afterlife (died 2010, inducted to Hall in 2012).
1998 – The Hall selected Don Sutton. We skip him, and then skip Perez, and Santo, and then its welcome to the Hall of Fame, Jim Rice. We’re already under 50%, so he’s all alone, but he doesn’t have to wait another decade until 2009.
2000 – Carlton Fisk is selected by the Hall, and we’re fine with that. Perez and Rice were next, and we already have them; our second place finisher is Gary Carter, but he is just under 50% and so will have to wait.
2002 – Ozzie Smith is really elected. Gary Carter is second, and now has over 50% of the vote, so he gets in a year earlier than reality.
2003 – Eddie Murray finished first, and was genuinely elected, and Carter was also elected. Since we already have Carter in, our second-place finisher is Bruce Sutter, who qualifies with 54% approval. In three years early.
2006 – Sutter was the only real inductee that year. Ignoring him, and second-place finisher Rice, our top recipient is Rich Gossage. And our second place finisher is Andre Dawson, and 61% makes him a qualifying second-placer. Gossage goes in for us now instead of 2008, and Dawson moves up from 2010.
2008 – Gossage was the Hall’s real choice. We’re going to go past him, and Rice, and Dawson, and find ourselves a nice shiny Bert Blyleven. The next finisher would be Lee Smith, but he’s under 50%; so Bert has the podium to himself now instead of waiting until 2011.
2009 – Rickey Henderson is taken in reality, as was Jim Rice. Our second place finisher (after skipping Rice, Dawson, and Blyleven) would again be Lee Smith, but again he’s under 50% and is not inducted.
2010 – Reality elects Dawson, but we’ve had him in for four years already. Next was Blyleven, also in already. Our top finisher in 2010 is Roberto Alomar, so he goes in a year ahead of time. Our second place finisher is Jack Morris, and he does receive 50% of the vote, so he goes in, too. Morris is the first person we’ve inducted who has not made the actual Hall. However, like Cepeda and Santo, similarly rejected by the BBWAA, he’s a near-cinch for a future Veteran’s Committee.
2011 – Reality selected Alomar and Blyleven, but we have beaten reality to the punch. Barry Larkin is our inductee. Morris would have gotten in again, but skipping over him means that, for the third time, our second place finisher is Lee Smith. And for the third time, he is under 50%.
2012 – The Hall really chose Larkin; we’ll ignore him, and then ignore Morris. Our number 1 becomes Jeff Bagwell. Our number two, again, is Lee Smith; but this time he picked up 50.6% of the vote. He’s in!
2013 – No one was selected by the BBWAA this year. Craig Biggio was on top the list, though, so he is in. We can ignore Morris, and Bagwell in third, to get down to Mike Piazza. He’s our second-place man, and he’s got 58% support, so in he goes.
So to summarize – this way guarantees that there will be someone to honor at Cooperstown each year. Players who aren’t selected in their first year tend to get in a couple of years earlier this way. Virtually all players who meet our rules but not the BBWAA 75% rule eventually get named to the Hall anyway. We’d have saved Orlando Cepeda and Ron Santo from the Veteran’s Committee. We would have inducted Jack Morris and Lee Smith, who have (definitely, probably) missed out from the BBWAA. We’ve already gotten to Bagwell, Biggio, and Piazza, who should all be eventual winners.
246/234 250/244 263/239 253/230 Colon(R) Gray(R) Parker(R) Straily(R) Avila (L) 154/270 255 260 273 263 Fielder (L) 284/282 267 271 285 274 Infante (R) 279/273 246 256 251 242 Cabrera (R) 388/343 309 322 315 303 Iglesias (R) 245/251 226 236 231 222 Dirks (L) 225/250 237 240 253 243 Jackson (R) 240/273 246 256 251 242 Hunter (R) 283/270 243 253 248 239 Martinez (B) 254/284 269 273 287 276 Net Tigers .2569 .2647 .2677 .2576 227/171 237/265 245/200 242/260 Scherzer(R) Verlander(R) Sanchez(R) Fister(R) Vogt(L) 263/290 253 264 273 270 Barton(L) 251/294 257 268 277 274 Sogard(L) 254/252 220 230 237 235 Donaldson(R) 358/296 195 302 228 296 Lowrie(B) 283/287 251 262 270 287 Cespedes(R) 303/241 159 246 185 241 Crisp(B) 245/307 268 280 289 307 Reddick(L) 245/264 230 241 249 246 Moss(L) 230/313 273 285 295 291 Net A's .2378 .2655 .2590 .2735
A very even series, with no team having a 60% or better chance in any game.
Game 1, Scherzer v Colon – Tigers 59.5%
Game 2, Verlander v Gray – A’s 50.4%
Game 3, Sanchez v Parker – Tigers 54.1%
Game 4, Fister v Straily – A’s 57.4%
Game 5, Scherzer v Colon – Tigers 59.5%
The total series leans to the Tigers, 55.7%
Here’s how their hitters and pitchers matched up:
Red Sox 228/255 171/254 245/226 278/272 Moore(L) Price(L) Cobb(R) Hellickson(R) Saltalamacchia(B) 229/299 282 320 Napoli (R) 302/283 296 295 246 296 Pedroia (R) 327/261 321 319 227 273 Middlebrooks(R)274/230 269 268 200 241 Drew (L) 205/304 180 135 286 325 Gomes (R) 277/265 272 271 Ellsbury (L) 245/308 215 161 290 329 Victorino(B) 299/270 293 292 254 289 Ortiz (L) 258/357 226 170 336 282 Ross(R) 273/203 268 267 Nava(B) 236/311 293 333 Net Sox .2650 .2538 .2723 .3008 231/252 240/265 188/203 252/227 Lester(L) Lackey(R) Buchholz(R) Peavy(R) Lobaton(B) 240/266 246 258 Loney(L) 265/287 235 265 208 278 Zobrist(B) 241/296 234 273 214 287 Longoria(R) 329/282 319 287 220 246 Escobar(R) 277/248 268 253 194 217 Rodriguez(R) 267/196 259 Jennings(R) 305/259 296 264 202 226 Myers(R) 308/286 299 292 223 250 Young(R) 266/262 258 267 205 229 Molina(R) 234/213 227 166 DeJesus(L) 111/282 260 204 273 Net Rays .2687 .2680 .2049 .2532
The matchups give the Rays a narrow advantage in three of five games, but the Red Sox advantage in the remaining two is so large that they get the benefit of the overall chances.
Game 1, Lester v Moore – Rays 51.7%
Game 2, Lackey v Price – Rays 56.8%
Game 3, Buchholz v Cobb – Red Sox 80.6%
Game 4, Peavy v Hellickson – Red Sox 70.3%
Game 5, Lester v Moore – Rays 51.7%
That works out to an expected Red Sox series victory 65.53% of the time.
Here’s how their hitters and pitchers matched up:
174/199 271/213 276/238 264/245 Kershaw(L) Greinke(R) Ryu(L) Nolasco(R) McCann (L) 227/307 152 320 241 312 Freeman(L) 285/339 191 353 303 344 EJohnson(B) 216/211 165 220 198 214 CJohnson(R) 338/280 259 229 309 264 Simmons(R) 253/254 194 208 232 239 Gattis(R) 285/268 218 220 261 253 Upton(R) 172/231 132 189 157 218 Heyward(L) 288/279 193 291 306 283 Net Braves .1863 .2519 .2478 .2595 267/249 211/246 294/219 303/240 Medlen(R) Minor(L) Teheran(R) Garcia(R) AEllis(R) 261/259 248 247 218 239 Gonzalez(L) 278/304 312 226 344 354 MEllis(R) 279/251 240 264 211 232 Ramirez(R) 387/353 338 366 297 326 Uribe(R) 285/289 277 270 243 267 Crawford(L) 205/300 308 166 339 350 Schumacher(L) 241/259 266 196 293 302 Puig(R) 336/320 306 318 270 295 Net Dodgers .2772 .2558 .2709 .2875
The matchups give the Dodgers an advantage, not just in every game, but in every permutation of matchups except a Minor vs Nolasco battle.
Game 1, Kershaw v Medlen – Dodgers 87.9%
Game 2, Greinke v Minor – Dodgers 51.9%
Game 3, Ryu v Teheran – Dodgers 61.0%
Game 4, Nolasco v Garcia – Dodgers 62.5%
Game 5, Kershaw v Medlen – Dodgers 87.9%
That works out to an expected Dodger series victory 85.45% of the time.
Matchup eqas, based on both the hitters and pitchers splits:
Wainwright Lynn Kelly Miller Martin 248 256 277 243 Morneau 264 321 286 310 Walker 270 328 291 316 Mercer 220 227 246 216 Alvarez 270 328 291 316 Marte 244 252 273 240 McCutcheon 287 297 322 283 Byrd 263 272 294 259 Net .2377 .2646 .2644 .2537 (includes a .100 pitcher) Burnett Cole Liriano Morton Carpenter 361 312 167 413 Beltran 344 297 267 394 Holliday 268 303 311 256 Adams 342 295 130 391 Molina 246 278 323 235 Jay 310 268 134 355 Freese 218 247 303 208 Descalso 277 239 111 317 Net 2886 2599 2314 2966
Game 1: The Cardinals, behind Wainwright, are a huge favorite over Burnett and the Pirates in game 1. He’s got a huge lefty split, and the Cards will throw 5 against him (counting Beltran). A .2886-.2377 eqa margin equates to a 72.5% win percentage for the Cards.
Game 2: Lynn vs Cole: This time it is the Cardinal pitcher who has a big platoon split, but the Pirates can only send 3 lefties up to the plate (unless they choose to send Jones up over Marte). It makes for what should be the evenest matchup of the series; the Pirates have a .2646-.2599 eqa advantage, which comes out to a 52.2% chance for the Pirates to win game 2.
Game 3: All those lefties spell trouble for the Cards in game 3, as they have to face Francisco Liriano and his extraordinary lefty-killing splits. Meanwhile, Kelly offers the Cardinals nothing special. Its a .2644-.2314, Pirates, and that makes a 66.1% win chance for the Pirates.
Game 4: But all those Cardinal lefties come back in game 4, because Morton has even worse splits than Burnett. The Cardinals against Charlie stack up as a .2966 eqa, against the .2537 the Pirates manage against Shelby Miller. If it is Miller – Wacha’s numbers would come through as better than Miller’s, so the odds would only go up from the 68.6% in the Cardinal favor.
Game 5 figures to repeat game 1. <Edit: Ah, the Pirates swict to Cole for game 5. That makes it a much tighter .2599-.2377 Cardinal advantage, 61% instead of 73%…without taking into account the rest advantage for the Cardinals.>
Stick those percentages in with a random number generator, and the Cardinals are projected to win the series 67.6958% of the time.
And, with game 1 in the books and a 9-1 win, the Cardinals are up 78% to win the series.
Same setup as with the Reds/Pirates yesterday.
Danny Salazar goes 225 v left, 253 v right, and is RH.
Molina (R) 214 v RH, Salazar 253, net .208
Loney (L) 285 v 225 = 247
Zobrist (B) 295 v 225 = 255
Escobar (R) 252 v 253 = 245
Longoria (R) 281 v 253 = 273
DeJesus (L) 282 v 225 = 244
Jennings (R) 255 v 253 = 248
Myers (R) 291 v 253 = 283
Young (R) 259 v 253 = 252
Team total, .2505. Myers and Longoria are the strengths, Molina the notable weak link.
For the Indians, against Alex Cobb. Cobb is also right-handed, with a 245/226 left/right split.
Gomes (R) 272 v 226 = 236
Santana (B) 293 v 245 = 276
Kipnis (L) 291 v 245 = 274
Cabrera (B) 261 v 245 = 246
Aviles (R) 247 v 226 = 215
Brantley (L) 272 v 245 = 256
Bourn (L) 250 v 245 = 236
Swisher (B) 249 v 245 = 235
Giambi (L) 243 v 245 = 229
Team total, .2459.
[Late edit: so it seems it will Chisenhall at third instead of Aviles; he gets 252 v 245 = 237, Likewise, Raburn will play RF, pushing Swisher to 1B, Santana to DH, and iambi to the bench. So, effectively, Raburn (282 v 226 = 245) instead of Giambi. That changes the team total to .2490, which gives the Rays a .508 chance - prior to home field advantage.]
That difference spells a narrow Rays advantage, about a .523 win percentage.
Which I used as my title. However…
I haven’t accounted for home field, which should amount to about +.020 for Cleveland and -.020 for the Rays, which puts us at .503-.497 for the Rays. Nor have I accounted for (or, frankly, have any idea how to quantify) fatigue, as the Rays have been bouncing around the eastern half of North America while the Indians stayed home.
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