These are the dates when teams have first hit either a 100% or a 0% chance of making the playoffs in 2023 (according to my Playoff Odds; click on team name for their day-by-day chances). It is not the same thing as mathematically clinching or eliminated – but does represent the 1 in a million point.

Hit 100%:
Atlanta Aug 9
LA Dodgers Aug 30
Baltimore Sep 9

Hit 0%
Oakland May 24
Kansas City July 8
Colorado July 11
Chicago WS Sept 2
LA Angels Sept 6
Washington Sept 6
St Louis Sept 10
Pittsburgh Sept 12

Oakland’s elimination on May 24 – after just 50 games played – is the quickest elimination since at least 2004. But prior to 2007 I was using a different system, one that was (IMO) too rigid in its assessment of teams, so at the bare minimum we’re comparing Granny Smith to Galas. The previously fastest eliminations (since 2007) were Baltimore (63 in 2018, 64 in 2010) and Miami (65 in 2013).

Here are the spring training stats for 2023 (thorugh 3/15), compared with the (shortened) 2022 spring training totals.

In Arizona

And in Florida

First things first – we have a massive conflating factor called the World Baseball Classic this year, which has changed the personnel of the leagues rather dramatically. That is going to affect all of the conclusions, but for the moment we are going to pretend it is irrelevant.

The biggest change by far is the increase in stolen bases. The rule change limiting throws to first, as well as the larger bases, is resulting in far more stolen base attempts accompanied by a higher success rate. On a per-man on first basis (singles + walks + HBP):

Attempts/MO1       2022        2023

Arizona                     9.34%    11.96%     UP 28%

Florida                      7.31%    11.09%     UP 52%

Steals/MO1            2022        2023

Arizona                    6.84%      9.77%    UP 43%

Florida                     5.35%      8.74%    UP 63%

Stolen base rates haven’t been that high since 1919 (combined AL/NL numbers for 1901-2022; the last spike is spring stats 2023). The SB rate this spring is higher than the combined major league totals of the 1980s, and you literally have to go back to when Babe Ruth was still pitching to find higher numbers.

And all of this is happening with a success rate of over 80%, which is a rate unheard of in major league history (and suggests that teams aren’t running enough). I think I now consider it likely that somebody will steal over 60 bases this year. I am not sure who would do that, and I’m not sure I’d bet on a 70 just yet. But if you take a look at the early 90s – before the strike, after slide steps had cut into the stratospheric stolen base totals of the 80s – the league leader was putting up numbers between 70-78, while the third-best mark was in the 54-64 range. My best guess right now is that by the end of 2023 we will see numbers about like that.

As for the other big rule channges, it is harder to draw conclusions. Batting averages are up slightly (from .266 to .270 in Arizona, and .251 to .253 in Florida), but I’m not sure that is outside the noise window. Strikeouts (which you might think would go down, as the pitch clock gives pitchers less rest between pitches) are down slightly in the desert but up by a slightly higher amount in Florida, so a wash.

Florida
https://claydavenport.com/stats/webpages/2023/2023pageGrprealALL.shtml

Arizona
https://claydavenport.com/stats/webpages/2023/2023pageCacrealALL.shtml

The stats projections for the 2023 season are now up. Actually they first went up around Christmas, but I’ve been doing an unusual amount of tinkering this time around. Updates should happen at least a couple of times a week between now and the start of the season – which kicks off on March 30 this year.

1. Rules changes for 2023.

Trying to figure out how the reduced shifting and the new pickoff attempt rules will affect the statistics was definitely an exercise. A lot of it, admittedly, is guesswork, but we do have the results from the minor leagues to guide us. The gist of it is I raised stolen base attempts for all players by 25% (which obviously makes a bigger difference for guys who steal 20 bases than guys who steal 2), while decreasing caught stealing rates. Left-handed hitters received improved single rates, enough to raise their batting averages by about 10 points. Switch-hitters got a bout a 7-point raise. Righties get nothing. And similar adjustments were made for pitchers, with right-handed pitchers (who face more lefties) getting a slightly larger downgrade on their BABIP.

2. Pitcher adjustments

Every year, I make adjustments made to the way I translate between the player statistics and what I expect in the next season. The changes in the pitcher numbers are somewhat larger this year. I made a mistake last year, which did not become fully obvious to me until later, in that I way over-did the regression to mean aspect. All pitchers, good, bad, or otherwise, got squeezed into a tight bin, with not nearly enough variation. I tried to avoid that trap this year.

3. Stronger adjustments for foreign leagues

There are some oddities in projecting statistics from Japan, and I took a deep dive into them this year after a disappointing performance from some more disappointments. I’ve always calculated league strength by using the results of all players who played in those leagues. I had recognized a problem with that before – that the translations between AAA and Japan gave very different results than those between the majors and Japan – but extended it this year to noting where players are from. The result of that is Japanese hitters losing about 20 points of EqA in their translation. I will try to drw that out in another post.

4. Fielding changes

A friend of mine pointed out that the fielding numbers in the translation seemed off. This was something that I think I’ve known for a while. I had built in a sort of universal fielding adjustment to handle “defense” without having to track the position closely, and it just didn’t work. So I totally overhauled the way that worked, which should lead to more consistent values.

5. New schedules

The first set of numbers I put out still had the 2022 schedule built into it. Most years that is no big deal, but this year they are changing to a more balanced schedule, with less concentration on your own division. That was good for teams in the strong Eastern divisions (the Nationals gained 0.77 wins when I changed the schedules, most of any team; the Marlins, Red Sox, and Orioles all gained over 0.5 wins). And it was bad for the AL Central (the Guardians and White Sox were the two biggest losers, dropping by 0.85 and 0.69 wins). Those changes in team totals will be accompanied by changes in individual numbers, but it isn’t large enough to be a real issue.

6. org Files with Roster Status

One of my personal favorite tools while gearing up for the season are the org dt pages. Each team is broken down by position (with pitchers grouped into Starters, Relievers, and Swing), and each player is ranked in order of their projected Major League playing time. I find it a good way to see a team’s entire depth chart – including their coming league players. And I now have it cross-referencing with MLB roster status, so you can see who is on the 40-man roster (“A”, until we get into the season), who is an NRI (“n), and who is injured (“D”, when we get that far). Here are Brewer shortstops:

    SS
  Last         First       Team Lg  Age  PA AB   R    H  2B  3B  HR RBI  BB  SO  SB  CS   BA    OBP   SLG  EqBA EqOBP EqSLG  EqA   VORP  WARP Defense FRAA  MJ BRK IMP CLP ATT DRP UPS 
A Adames       Willy        MIL NL  27  597 543  81 147  28   2  28  89  53 170   7   4 0.271 0.337 0.484 0.272 0.340 0.488 0.284  43.1   5.2 140-SS   4    94  10  42  35  18   8 133 
n Alvarez      Eddy         MIL NL  33  433 386  50  99  16   2  10  39  35 117   7   2 0.256 0.337 0.386 0.266 0.345 0.394 0.264  20.6   2.2 100-SS  -1    61  16  36  56  26   8  20 
A Turang       Brice        MIL NL  23  625 576  69 152  25   3  10  60  49 143  22   3 0.264 0.322 0.370 0.264 0.324 0.363 0.256  25.1   3.4 147-SS   5    40  27  57  30   8   1  54 
n Monasterio   Andruw       MIL NL  26  437 398  53  97  18   2   9  43  35 116   9   2 0.244 0.311 0.367 0.258 0.326 0.375 0.249  14.1   1.3 102-SS  -3    34  23  56  48  25   6  33 
  Devanney     Cam          MIL NL  26  587 539  59 117  26   2  17  59  41 165   5   2 0.217 0.281 0.367 0.231 0.294 0.376 0.233  10.1   0.9 137-SS  -2    24  31  52  41  24   5  32 
  Brown        Eric         MIL NL  22  305 278  33  58  14   2   6  23  22  81  19   2 0.209 0.279 0.338 0.226 0.295 0.343 0.237   6.4   0.8  71-SS   1    15  23  40  40   1   0  50 
  Zamora       Freddy       MIL NL  24  231 218  20  46   6   1   3  13  10  63   6   1 0.211 0.255 0.289 0.211 0.254 0.284 0.202  -2.7  -0.4  54-SS  -1     5  64  74  27  20   1   5 
  Garcia       Eduardo      MIL NL  20  541 516  52 110  22   2  13  53  20 217  12   1 0.213 0.250 0.339 0.227 0.263 0.350 0.214  -0.5  -0.3 127-SS  -2     2  43  65  30   8   4  15 
  Murray       Ethan        MIL NL  23  427 393  38  79  16   2   8  35  31 137   9   2 0.201 0.265 0.313 0.215 0.279 0.315 0.214  -0.7   0.5 100-SS   5     2  28  51  37  13   2  11 
  Barrios      Gregory      MIL NL  19  363 341  36  78  13   2   1  28  19  82  13   1 0.229 0.275 0.287 0.233 0.279 0.289 0.214  -0.4   0.9  85-SS   8     0  61  76  22   6   1  13 
  Guilarte     Daniel       MIL NL  19  266 246  20  54   9   1   1  21  20  79   9   2 0.220 0.278 0.276 0.228 0.288 0.281 0.211  -1.0  -0.3  63-SS  -2     0  49  62  27   3   0  13 

I don’t know more than anyone else when games might start being played…but I do have my first look at team projections here.

Just like last year, it looks like the Dodgers and Padres are the best two teams. The Padres collapsed, epically; the Dodgers made it to the LCS before losing to the (eventual WS champ) Braves.

My spare time isn’t what it used to be…NOAA keeps me busy. But I finally do have the new MLB pbp files parsed enough to run all the split data I need. So all of the player, league, and team should be back to having those breakdowns. Not a fan of MLB’s chosen format, although to be fair I am not using their chosen tools for reading it out. It is really, really non-linear; I think my code has to make 5 separate passes through a game to get everything it needs and arranged in a game order.

Cool graph today is the playoff picture of the AL East:

The Rays (green) remain steady at the top. The Yankee (black) line surges through their August winning streak, but has been collapsing since. The Blue Jays track (blue) over the last month is a mirror image of the Yankees, with the Red Sox (red) line running right between both of them. There is a little better than a 4-in-5 chance that both AL wild cards come from the East…two out of three teams that right now are basically even.

Also in playoff odds…the Giants this morning became the first team to make the playoffs in all 1 million runs, giving them a 100% score. The Dodgers fell just short; their 99.99995 means they finished playoff tie in 1 out of a million runs, and made the playoffs in the other 999,999 runs.

The “DTs by league” pages are not updating right now, because I can’t get the play-by-play data I use to build them from MLB. Trying to figure out what’s gone wrong in my connections. The EQA report is mostly fine – except for splits data which, once again, requires the PBP files.

These come from my million-run Postseason Odds page, which will update daily through the season.

Average wins by position in AL East:  100.4 91.4 84.8 77.4 58.1 
AL East    Pct3  Avg W  Avg L  Champions  Wild Card   Playoffs 
Yankees    .593   97.5   64.5   63.58508   23.74102   87.32610 
Blue Jays  .529   86.1   75.9   13.51403   31.84269   45.35672 
Rays       .525   85.9   76.1   13.07292   31.31225   44.38516 
Red Sox    .518   84.2   77.8    9.81512   27.01932   36.83444 
Orioles    .370   58.4  103.6     .01285     .10404     .11689 

The Yankees are an overwhelming favorite to win the division. The 
Orioles have the lowest playoff odds of any team in the majors. At 
least one of the remaining teams should take a wild card, and 
there's a pretty good chance that both wild cards come from the 
East.

Average wins by position in AL Central:  94.7 86.5 79.7 72.6 63.6 
AL Central Pct3  Avg W  Avg L  Champions  Wild Card   Playoffs 
White Sox  .528   88.7   73.3   43.41552   16.64380   60.05932 
Twins      .527   88.1   73.9   40.12318   17.02435   57.14754 
Clevelands .478   79.6   82.4   11.56375    9.88443   21.44818 
Royals     .444   73.7   88.3    3.96877    3.91370    7.88246 
Tigers     .406   67.1   94.9     .92878     .95012    1.87890 

The Twins and White Sox are rated pretty close to even, with me 
giving the barest edge to the White Sox. The ex-Sockalexises have a 
chance, if their pitching can carry them through. The Royals have 
their best chance in several years, and some prospects whispering 
hopes of even better times ahead. I expect the Tigers to challenge 
the Orioles for the barrel bottom.

Average wins by position in AL west:  96.7 85.3 78.9 73.1 65.7 
AL West    Pct3  Avg W  Avg L  Champions  Wild Card   Playoffs 
Astros     .576   95.2   66.8   77.30096    7.74413   85.04509 
Athletics  .489   80.0   82.0   10.00943   12.37409   22.38351 
Angels     .477   77.4   84.6    6.30802    8.42805   14.73607 
Mariners   .461   75.2   86.8    4.22883    5.91044   10.13927 
Rangers    .441   71.9   90.1    2.15278    3.10758    5.26035 

The Astros get rated as the single most likely division champion, 
thanks to their own strengths and contenders' weaknesses. 

Average wins by AL First Wild Card:  93.1 
Average wins by AL Second Wild Card:  88.8 

Average wins by position in NL East:  95.8 88.5 82.5 76.0 65.3 
NL East    Pct3  Avg W  Avg L  Champions  Wild Card   Playoffs 
Mets       .557   88.7   73.3   36.46963   17.94029   54.40992 
Braves     .554   88.3   73.7   34.61201   17.93041   52.54242 
Nationals  .528   83.9   78.1   18.43439   14.48495   32.91933 
Phillies   .507   80.1   81.9    9.84990    9.46676   19.31666 
Marlins    .435   67.1   94.9     .63407     .78651    1.42058 

A highly contentious division, with almost everyone having a chance. 

Average wins by position in NL Central:  93.3 86.1 80.3 73.7 61.2 
NL Central Pct3  Avg W  Avg L  Champions  Wild Card   Playoffs 
Brewers    .529   86.1   75.9   36.00577   10.24474   46.25051 
Cardinals  .520   84.6   77.4   29.57912   10.05231   39.63143 
Reds       .507   82.3   79.7   21.18838    8.59095   29.77934 
Cubs       .491   79.2   82.8   12.87463    6.10106   18.97569 
Pirates    .394   62.4   99.6     .35211     .16244     .51455 

An even more contentious division, although the teams are all 1-5 
games behind the East group. It is the weakest division.

Average wins by position in NL West:  107.2 96.4 81.5 72.4 60.9 
NL West    Pct3  Avg W  Avg L  Champions  Wild Card   Playoffs 
Dodgers    .646  104.0   58.0   63.67985   32.50205   96.18190 
Padres     .620   99.1   62.9   35.21897   54.38710   89.60607 
Dbacks     .498   77.9   84.1     .75033   11.05648   11.80681 
Giants     .480   74.8   87.2     .34225    6.02418    6.36643 
Rockies    .412   62.8   99.2     .00860     .26977     .27837 

What seems very likely is that the Dodgers and Padres - with the two 
best projected records in the majors - will both advance to the 
playoffs, leaving the rest of the NL to fight over the remaining WC 
spot. 

Average wins by NL First Wild Card:  97.4 
Average wins by NL Second Wild Card:  90.0

Although things don’t look much different, if we dive down under the hood you will be able to see that I have been a lot of work in the background. And I guess one of the best ways to demonstrate is to look at this page here:

http://www.claydavenport.com/ht/TORKELSON19990826A.shtml

I am now able to provide a stat-based projection on 2020’s #1 overall pick, Spencer Torkelson, based off of his college statistics. Without them I had literally nothing on Torkelson to base any stats. With them – that is a projection that makes him a very legitimate callup.

And he is not alone. Adley Rutschmann jumps from a .240 eqa projection to .284 – on the basis of his college pedigree. Andrew Vaughn likewise goes from the mid-230s to the mid-270s – because now I know he had a pair of .300 translated seasons in college.

I’m still early in my use of college stats – haven’t gotten fielding info in yet, which is going to make bring Rutschmann’s drop once it knows he’s a catcher – but it seems to work as well as minor league numbers, with a couple of caveats.

I haven’t backed these up with a formal study yet, but it certainly looks like there is a very tendency for a player’s final year in college to be a lot better than his prior years. Rutschmann and Torkelson both fit that pattern. I think that pattern will be especially pronounced for 2020 – play was interrupted typically before in-conference games started, meaning that teams (from the major conferences, at least) played unusually weak schedules. The way that I calculate the difficulties, averaging across different years, is going to mask that weakness.

The second caveat is that players appear to underperform their college numbers in their first taste of pro ball, but tend to recover towards their college averages in later seasons. There is this double whammy of a player’s final college year coming in too hot, and his first pro year too cold, that creates a LOT of distortion.

About the difficulty ratings. So the ratings I am using here see the top three college conferences as the Southeastern, the Pac-12, and the ACC. This is in line with the number of players who have gone on from these conferences to play in Organized Baseball. I counted 761 SEC players in OB ranks since 2011, 611 ACC players, and 565 from the Pac-12. Their ratings, of .498, .458, and .478 are comparable to the short-season Northwest (.469) and New York-Penn (.514) leagues (all of these numbers area average from 2015-2019). Your Midwest and Sally leagues in the .55-.57 range, the high A leagues are .63 give or take, with the majors sitting at just above 1.00 right now.

Here’s how the top 16 conferences, ranked by number of OB players, rank:

Conference       Players Diff
             -->  NY-Penn   .514
 Southeastern      761 0.498
 Atlantic Coast    611 0.458
 Pacific 12        565 0.478
             -->  Northwest .469
             -->  Pioneer   .464
             -->  Appalachian .462
 Big 12            464 0.432
 Conference USA    364 0.443
 Big West          347 0.433
 Big 10            340 0.415
             --> Gulf Coast  .423
             --> Arizona     .418
 Sun Belt          274 0.407
 West Coast        263 0.420
 Southland         258 0.388
 Big East          266 0.432
 Western Athletic  242 0.372
 Missouri Valley   219 0.432
 Mountain West     217 0.435
 American Athletic 214 0.413
 Atlantic Sun      205 0.411

There is a .81 correlation between the number of players taken from each league and its quality, so the draft appears to be resosnably efficient The ratings do tend to keep dropping from there. By the time you get to, say, the Wisconsin Intercollegiate League, made up of the smaller schools of the University of Wisconsin system (like Oshkosh, Stevens Point, or Whitewater), the rating is coming in at just .130.

Hat tip to baseballreference.com, who really pulled these collegiate numbers together. My task was just to pull them together in a sensible fashion

Major League Baseball has never had a player whose last name starts with X. Joe Xavier, who made it to AAA in 1989-90, is as close as any X player has gotten.

Until now.

Major League Baseball’s decision to reclassify some of the Negro Leagues  as “major league” changes that.  The Negro American League – once of the reclassified leagues – had a man by the name of Leovigildo Xiques, who played for the (Cincinnati and) Indianapolis Clowns.