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You may recall that around this time last year I explained to the world and everyone why I was Through With Pythagoras. I had long given serious attention to the teaching of the old desert sage, but those days were now behind me.


 I shall now copy and paste for a while...

*****************************

We are more or less agreed that sometimes a team's W-L record doesn't tell their story clear and true. And when it doesn't, we know why. It's because the team's record in either one-run games or blowouts (or both) varies somehow from their performance the rest of the time.

These days even ESPN's home page includes Runs Scored and Allowed and Run Differential. As if that told the story (after all, a run differential of 100 runs in Dodger Stadium is very, very different from the same thing in Coors Field.)

From a team's Runs Scored and Allowed we extrapolate what we have come to call a team's Pythagorean W-L record. This is based entirely - entirely - on the relationship between total Runs Scored and Allowed. As I suppose is generally known, there are two fairly common methods of making that calculation: one involves squaring the numbers involved, while the other uses a component, often 1.83, instead. Whichever you use is entirely up to you. Let me hear no talk of accuracy.  Whichever formula you choose generates a fiction, an imaginary W-L record. One fantasy is not more accurate than another. It's all a matter of which one you like best, or which one suits your needs. (As I was generally trying to identify real seasons that didn't tally closely with the actual results, I very much preferred the traditional formula that squared the numbers. If you're looking for seasons that deviate from one's reasonable expectations you don't want to use a method that generates those deviant seasons willy-nilly. Which is what using the component will do.)

Now there are two problems with using one of the Pythagorean formulae to generate a W-L record, as imaginary as it may be.

The first problem  is with the blowout games that constitute a significant part of any team's season. It's not that it makes no difference at all whether you win by 6 runs or 12 - but I do suspect that this mostly tells you something about choices made by the losing team when a game gets out of hand rather than anything about the quality of either team. So I think that while a team's record in blowout games is very significant, I don't think a team's Runs Scored and Allowed in those games is nearly as important. And what this means is that the raw data - the Runs Scored and Allowed - that is being fed into the Pythagorean formula of your choice creates its own distortions right from the jump.

The other issue, which I think is much more important, is pretty obvious. Of course it involves my own Great White Whale. But nevertheless, here we go! Because baseball teams play lots of games that are decided by a single run. And even if you do believe that Run Differential and the Pythagorean formula will give you an accurate idea of a team's quality, you still can't apply it to one-run games. You simply can't do that.

Because that's not how one-run games work.

It just isn't. You can't apply a Pythagorean formula to those games. Because in one-run games, the impact of random chance is sufficient to overcome the impact of team quality. You may not be able to win a game by ten runs thanks to a lucky bounce. But you can definitely win by one-run.

This is why the effect of one-run games is to drag every team to the centre. It drags everyone towards .500 - it lifts the bad teams and it lowers the good teams. That's what it does. This is a Law.

This doesn't quite mean that we should set a .500 record in one-run games as a team's expected outcome. The better teams actually do play better in one-run games than the bad teams. It's just that any single season is much, much too short a sample for that result to manifest itself. It would be exactly like assessing a hitter's season on 30 random plate appearances. We need the whole season, we need the 700 plate appearances to have a decent idea. As it happens, that's about how many one-run games it takes for a team's quality to begin to consistently affect that team's record in one-run games.

And even when we have that many games (in truth, the number needed might be closer to 1,000 games) - a team's record in one-run games still isn't going to match whatever Pythagorean projection we had come up with. Because one-run games are still going to drag teams towards .500, however good or bad they may be. That's what they do. It's just that if you play enough of those games, the effect won't be as pronounced. Given enough games, an equilibrium between these two forces is eventually reached, between the the relentless pull towards .500 and the actual quality of the teams. 

The effect is always present, and it's generally reliable. To state it very crudely, the .600 teams will play something like .550 ball in one run games, the .550 teams will play something like .530 ball in one-run games, the .450 teams will play something .480 ball in one-run games. There is a reasonable expectation that teams of a specific quality will provide a specific level of performance in one-run games. We know this because we have more than a century of data that tells us so. Here is the data (through 2015)


                                  OVERALL                             ONE-RUN GAMES                            OTHER GAMES        
                                                                            
Record    No. of Teams        GPL      W        L      PCT            GPL       W        L      PCT          GPL        W         L     PCT
                                                                            
.700 plus     10             1,530   1,100      430    .719            393      256      137    .651        1,137      844       293    .742
.650-.699     43             6,556   4,394    2,162    .670          1,931    1,158      773    .600        4,760    3,236     1,389    .700
.625-.649     68            10,576   6,723    3,853    .636          3,106    1,837    1,269    .591        7,470    4,886     2,584    .654
.600-.624    133            20,686  12,651    8,035    .612          6,195    3,488    2,707    .563       14,491    9,163     5,328    .632
.575-.599    199            30,987  18,235   12,752    .588          9,305    5,230    4,075    .562       21,682   13,005     8,677    .600
.550-.574    233            36,253  20,401   15,852    .563         10,983    5,898    5,085    .537       25,270   14,503    10,767    .574
.525-.549    325            51,352  27,572   23,780    .537         15,589    8,103    7,486    .520       35,763   19,469    16,294    .544
.500-.524    258            40,532  20,677   19,855    .510         12,445    6,252    6,193    .502       28,085   14,425    13,662    .514
.475-.499    238            37,403  18,166   19,237    .486         11,472    5,655    5,817    .493       25,931   12,511    13,420    .482
.450-.474    256            39,927  18,439   21,488    .462         12,149    5,792    6,357    .477       27,778   12,647    15,131    .455
.425-.449    202            31,600  13,823   17,777    .437          9,746    4,529    5,217    .465       21,854    9,294    12,560    .425
.400-.424    173            27,129  11,166   15,963    .412          8,157    3,687    4,470    .452       18,972    7,479    11,493    .394
.375-.399    113            17,554   6,800   10,754    .387          5,320    2,253    3,067    .423       12,234    4,547     7,687    .372
.350-.374     62             9,520   3,446    6,074    .362          2,828    1,172    1,656    .414        6,692    2,274     4,418    .340
.300-.349     81            12,500   4,145    8,355    .332          3,712    1,479    2,233    .398        8,788    2,666     6,122    .303
.000-.299     20             3,057     843    2,214    .276            909      331      578    .364        2,148      512     1,636    .238
                                                                            
            2414           377,162 188,581  188,581    .500        114,240   57,120   57,120    .500      263,055  131,461   131,461    0.500

As you can see, the pattern is pretty dependable. The only group that doesn't fit perfectly includes the 133 teams who played between .600 and .624 ball. Those teams had results in one-run games that were just a little bit worse than all this would lead you to expect. They played 6195 one run games, and I would have expected them to win about 3560 of them (roughly .575 ball) - they actually went 3488-2707, which is just .563 ball. How big a deal is that? Not all that much, actually - it's exactly the same as the difference between going 93-69 and going 91-71.

But I certainly didn't want to create a dozen different formulae, depending on a team's WL record, in order to derive expected outcomes in one-run games. That would be... extremely tedious. I wanted a nifty, catch-all that I could apply to everyone. And so I developed a very, very simple formula to generate a team's projected record in one-run games. I'm sure it's somewhat crude, but It's as consistent as I could hope it to be once the sample gets large enough.  Once the sample gets large enough, the formula actually works (I can hardly believe it myself!)  I don't need to worry about the blowouts. I don't even have to worry about total Runs Scored and Allowed. Simply adjusting the outcomes of one-run games turns out to be enough to make the actual results match up with the expected results.

Here's how it works (three calculations are involved!)

In 2021, Tampa Bay went 20-25 in their one-run games. They played .684 ball (80-37) the rest of the time. So:

1) Multiply their 45 one-run games by their .684 winning percentage in their Other Games. You get 30 (because I'm using the INTEGER function, I don't want to mess around with 30.7 - hey, you either win or you don't!)

2) Multiply those same 45 games by .500 - after all, dragging every team towards .500 is precisely what one-run games do. It's what they're for. This time we get 22 (the INTEGER function strikes again, lowering 22.5 to 22).

3) Add the two figures - 30 and 22 - and divide them by 2. Easy enough, it's 26.

Voila! Tampa Bay's expected W-L record in one-run games is 26-19 instead of the 20-25 inflicted on them by Cold Reality.  We are free, if we like, to regard this as more reflective of that team's quality than What Actually Happened.

****************************

I have, naturally, carried out the same operation on the season just concluded and I am ready - nay, I am eager - to tell everyone what You Just Saw!

This, I submit, is a better reflection of true team quality than the actual standings (or anything Pythagoras shoots out of his rear end.)

               ADJUSTED RECORD       NOT ONE-RUN GAMES    ACTUAL ONE RUN GAMES    ADJUSTED ONE RUN GAMES        
                                                           
     W    L    PCT        W    L    PCT        W    L    PCT        W    L    PCT
                                                           
NY Yankees    102  60   .630        68  36   .654        31   27   .534        34   24    .586
Toronto    89  73   .549        62  50   .554        30   20   .600        27   23    .540
Tampa Bay    88  74   .543        59  49   .546        27   27   .500        29   25    .537
Baltimore    85  77   .525        60  55   .522        23   24   .489        25   22    .532
Boston    79  83   .488        54  58   .482        24   26   .480        25   25    .500
                                                         
Cleveland    88  74   .543        64  53   .547        28   17   .622        24   21    .533
Minnesota    82  80   .506        58  56   .509        20   28   .417        24   24    .500
Chicago    75  87   .463        54  65   .454        27   16   .628        21   22    .488
Kansas City    65  97   .401        49  77   .389        16   20   .444        16   20    .444
Detroit    62 100   .383        44  76   .367        22   20   .524        18   24    .429
                                                          
Houston    104  58   .642        78  40   .661        28   16   .636        26   18    .591
Seattle    85  77   .525        56  50   .528        34   22   .607        29   27    .518
Los Angeles    78  84   .481        55  61   .474        18   28   .391        23   23    .500
Texas    78  84   .481        53  59   .473        15   35   .300        25   25    .500
Oakland    61 101   .377        43  77   .358        17   25   .405        18   24    .429
                                                         
NY Mets    101  61   .623        80  46   .635        21   15   .583        21   15    .583
Atlanta    100  62   .617        75  43   .636        26   18   .591        25   19    .568
Philadelphia    91  71   .562        65  50   .565        22   25   .468        26   21    .553
Miami    76  86   .469        45  53   .459        24   40   .375        31   33    .484
Washington    54 108   .333        38  85   .309        17   22   .436        16   23    .410
                                                          
St Louis    94  68   .580        77  54   .588        16   15   .516        17   14    .548
Milwaukee    85  77   .525        58  53   .523        28   23   .549        27   24    .529
Chicago     73  89   .451        48  61   .440        26   27   .491        25   28    .472
Pittsburgh    62 100   .383        41  73   .360        21   27   .438        21   27    .438
Cincinnati    60 102   .370        41  77   .347        21   23   .477        19   25    .432
                                                          
Los Angeles    114  48   .704        95  36   .725        16   15   .516        19   12    .613
San Francisco   85  77   .525        59  54   .522        22   27   .449        26   23    .531
San Diego    83  79   .512        59  56   .513        30   17   .638        24   23    .511
Arizona    80  82   .494        57  59   .491        17   29   .370        23   23    .500
Colorado    66  96   .407        45  70   .391        23   24   .489        21   26    .447

We would still have the same six teams meeting in the AL post-season, but the match-ups would be slightly different. Toronto would be hosting Tampa Bay in the fight to see who gets to play Houston. And Seattle (who edge out Baltimore with the tie-breaker) would be off to Cleveland.

The most interesting AL team, by a mile, is Texas. Normally the one-run games help losing teams - they drag their records up towards .500. But in the tiny sample that is any single season, literally anything can happen. Like the Rangers going 15-35 in one-run games. I think we all understand that this was simply karmic payback for their equally inexplicable record (36-11) in these games back in 2016, but poor Chris Woodward paid a terrible price for this random misfortune.

These adjustments really shake up the NL post-season. The Braves, by losing just one of their wins, end up as with a Wild Card berth instead of the division title. They will take on... San Francisco? Yup - the Padres fall to third place in the NL West and miss the post-season entirely. The Giants hold the tie-breaker over Milwaukee.

Gosh. The Astros and the Dodgers are the best teams in the game? Who knew?
       
What Did We Just See | 27 comments | Create New Account
The following comments are owned by whomever posted them. This site is not responsible for what they say.
bpoz - Thursday, October 06 2022 @ 09:34 AM EDT (#423287) #
Thanks Magpie.
Joe - Thursday, October 06 2022 @ 09:56 AM EDT (#423288) #
Magpie, what does your adjusted record correlate better to? You wrote "Simply adjusting the outcomes of one-run games turns out to be enough to make the actual results match up with the expected results", but I'm not sure what those expected results are.
Magpie - Thursday, October 06 2022 @ 11:29 AM EDT (#423291) #
How to explain. Well, I wanted to discover what we should expect a team of a certain quality - say, a team that wins .700 of their games - to play in their one run games. Well, there have only been ten such teams, but they played a total of 1530 games, with an overall winning percentage of .719. In their 393 one-run games, they played .651 ball. So that's what we should expect.

That's the upper end - at the lower end, we'd have the teams that played less than .300 ball - they played .276 in over 3,000 games overall, but .364 in almost 1,000 one-run games.

And we have all the points in between (and far, far larger samples to work with as well.) It's easy enough to see a predictable relationship between the overall record of teams of a certain quality, and the record in one-run games of teams of that quality. Those games always drag teams towards .500 either pulling them down or lifting them up.

So I came up with a nice simple (if not downright crude) formula that produced a result that duplicated what the enormous amount of data already told me was happening - that if applied to a team that played .700 ball would produce a record of .650 in their one-run games. And so on and so forth.

That was the idea!
Magpie - Thursday, October 06 2022 @ 11:34 AM EDT (#423292) #
SO the idea is - rather than following Pythagoras and generating a W-L record based on raw Runs Scored and Allowed - I'm using Wins and Losses in non-one run games. And using that to modify - but only modify - the results of the one-run games.
Chuck - Thursday, October 06 2022 @ 11:42 AM EDT (#423293) #
Any modelling done to remove random variation ("luck") is laudable, so huzzah.

Last year I employed a technique, as much art as science, to get to more representative team records. The premise was that teams score and allow many meaningless runs in a year, best exemplified in the 28-5 game. Why should all the excess runs in this game taint Pythagoras who considers all runs in a season to be of equal value?

So I recast the Jays' individual games, adjusting each game's score by lopping off excess runs to, arbitrarily, give 4-, 5- and 6-run gaps. So the 28-5 game was recast as 9-5, 10-5 and 11-5. Vanilla Pythagoras had the team winning 91 games, rather than their actual 92, and my modified Pythagoras had them winning 88 or 89, shaving off a few more wins yet.

bpoz - Thursday, October 06 2022 @ 01:27 PM EDT (#423295) #
Thanks again Magpie. SD won the trade deadline by acquiring J Soto so that deal should have improved their winning %. I don't think they did give up a lot of their ML talent. Baseball is hard to understand. It seems Washington played at the same .340 winning % but SD's winning % went down from .570 to .549.

Do you think the gain in offense was offset by a larger loss of defense? I don't know if pitching matters.

Also Baltimore if I recall correctly started to threaten us after the trade deadline even though they were sellers. I suppose the Rutschman/Henderson addition/improvement may answer that.
Magpie - Thursday, October 06 2022 @ 01:37 PM EDT (#423296) #
SD won the trade deadline by acquiring J Soto so that deal should have improved their winning %.

It would have if Soto had done any hitting. Literally, all he did in San Diego was draw walks. Soto hit 6 HR and drove in 16 runs in 52 games. And Josh Bell was awful. They were better off with Hosmer.
Magpie - Thursday, October 06 2022 @ 01:43 PM EDT (#423297) #
As for Baltimore - they were sellers, but they didn't give up much. Not really. They traded their Closer, but relief pitchers grow on trees and they already had plenty of useful relievers. And they traded a middle-of-the-pack DH in Mancini which let them take a look at kids like Stowers and Vavra who have a chance to help them going forward. Mike Elias may know what he's doing. Very troublesome.
Magpie - Thursday, October 06 2022 @ 01:52 PM EDT (#423298) #
It's a little stunning how what was by far the biggest move at the deadline played out. San Diego brought in Soto and Bell, and the two of them were less productive than the guys they replaced, Hosmer and Voit. Youneverknow. I suppose Brandon Drury was a bit of an upgrade over Nomar Mazara. But what a nothing burger that was.

So far.
hypobole - Thursday, October 06 2022 @ 02:13 PM EDT (#423299) #
Longo at FG's take on Jays Mariners.

https://blogs.fangraphs.com/al-wild-card-series-preview-blue-jays-vs-mariners/
Kasi - Thursday, October 06 2022 @ 02:16 PM EDT (#423300) #
Soto Iíve been wary on for a while. Heís a lot like Teoscar and while I like Teoscar and would want a better hitting version of him, I donít know if I want a better hitting version that has massively negative fielding and baserunning value at the cost of both what it cost to acquire him and what it will cost to extend him. I think heís fairly overrated and I think whomever extends him will regret it.
Magpie - Thursday, October 06 2022 @ 02:24 PM EDT (#423301) #
Soto's still very, very young and his BABiP this season was just unfathomably bad and unlikely to repeat itself. But he didn't hit as many balls hard, and he hit a lot more fly balls. We all get on Guerrero for all the groundballs, but ground balls can find holes in the defense. Fly balls just wait for someone to catch them.
hypobole - Thursday, October 06 2022 @ 02:50 PM EDT (#423302) #
Soto hit .221, 28 wRC+ with the Padres on his ground balls.
He hit only .176 on his fly balls, but even that puny production was good for 113 wRC+.

Now take Vlad, who did hit the ball hard.

GB .231 AVG, 37 wRC+

FB .299 AVG, 244 wRC+
bpoz - Thursday, October 06 2022 @ 03:05 PM EDT (#423303) #
The Soto talk is interesting.

A different approach to trade deadline acquisitions is IMO, just IMO, the trade AA made for Raisel Iglesias. This has to make the team a stronger playoff team. You face only good teams in the playoffs. "A chain is only as strong as it's weakest link". Iglesias is a great back up closer in case of injury or poor performance. Except Atlanta's pen was elite before Iglesias. Now it is even better. Ward/Henke 7,8,9 innings.
Mike Green - Thursday, October 06 2022 @ 03:59 PM EDT (#423305) #
The American League Base Runs standings:
HOU- 107-55
NYY-  104-58
TOR-  90-72
CLE-  89-73
TBR-  85-77
SEA-  85-77
MIN-   82-80
LAA-   81-81
CHW- 79-83
BAL-   78-84
BOS-  77-85
TEX-  77-85
KCR-  65-97
DET-  64-98
OAK- 57-105

Once you adjust for strength of schedule, the Jays were a considerably better team than the Mariners and they are very well suited to the playoffs (save for the absence of a couple of left-handed bats). 

Base runs adjusts not only for overperformance in translating runs scored and allowed to win, but also for efficiency or lack thereof in translating the elements of runs scoring and prevention into actual prevention (i.e. hit bunching). 
Chuck - Thursday, October 06 2022 @ 04:07 PM EDT (#423306) #
Soto has little in common with Hernandez.

By age 23, Soto has racked up 23 WAR in 2600 PA. By that age, Hernandez had racked up 100 PA and would not become a regular until age 25.

Soto's career OPS+ is 157, Hernandez 121. Soto is still years away from his theoretical peak.

Soto's OPS+ this season -- the very one causing consternation -- was 149. Hernandez came close to that mark exactly once, in the abbreviated 2020 season.

Kasi - Thursday, October 06 2022 @ 04:50 PM EDT (#423307) #
I disagree that Soto is years away from his peak. Players just donít magically go up up up. To me itís how long he maintains his current form as I donít see him besting what heís done before. By this argument Vlads last year was just a peek of what he could do in the future. I find it more likely that Vlads last year will probably be a career year and something he just wonít be able to break past more than say once or so. Trouts best two years were his first two years and since then itís been a slow regression against time and injury. Ofc you can say like Judge they can have a breakout season later and sure that could happen but the difference between Judge and Vlad/Soto is he is a far better player in the field and on the bases then they are.

And also disagree Soto and Hernandez donít have much in common. Theyíre both bat first OF with little defensive or base running value. If anything Soto is considerably worse there than Teoscar. I agree Soto has a far better bat but I just donít see him making enough of an impact on the field to be worth 35 million or whatever someone is going to pay him.
Chuck - Thursday, October 06 2022 @ 05:13 PM EDT (#423308) #
I used the term theoretical peak intentionally. I do concede that players who are great at a very young age -- Arod, Trout, Guerrero, Soto -- tend not to follow the traditional growth arc. In fact I argued that very thing last year commenting on Guerrero's season, throwing shade on Buck's "and he's only going to get better, folks" assertions. Still, Soto is a 6 WAR player who is all of 23. If he is never better than a 6 WAR player, well, that's still a great player. Perhaps you don't think that is worth 35M? Or perhaps you think he is now starting his decline?

And yes, Hernandez and Soto are poor defensive outfielders. They certainly have that in common. You could another hundred guys to such a list if that's the entirety of your criteria.

Magpie - Thursday, October 06 2022 @ 06:03 PM EDT (#423310) #
I would assume that Soto, like Guerrero, is still forming, still developing, still in the process of becoming whatever he's destined to become. Soto has been a little more all over the map - in his four full seasons, he's hit anywhere from .242 to .313 and he's just got good power, nothing really special. His real elite skill is his strike zone judgement. He'll probably hit for more power - they usually do - but he's also going to spend half his time in one of the best pitcher's parks in all of baseball which may (or may not, youneverknow) also affect his game.
hypobole - Thursday, October 06 2022 @ 06:03 PM EDT (#423311) #
I hate ground balls, unless we're pitching. MLB average was .235. 3747 RBI, 3374 GDP. However some batters do have success.

The qualified leader this season was Harold Ramirez, who hit a robust .347. 53 singles, 8 doubles for 176 grounders. Only 5 GDP's, 24 RBI's.

At the other end was another ex-Jay. Rowdy Tellez hit .131. 21 hits, all singles, for 160 grounders. 19 GDP's, 8 RBI's.
Magpie - Thursday, October 06 2022 @ 06:40 PM EDT (#423312) #
I hate ground balls

Well, I like flyball pitchers - most great pitchers are power pitchers, and most power pitchers are flyball pitchers. There are exceptions to this, from Roy Halladay to Sandy Alcantara - but Cole, Darvish, Cease, Scherzer have always got more flyballs than the other pitchers.

And I figure that if I like that type of pitcher - I shouldn't like that type of hitter. But I realize that you can't be stubborn about it. Obviously Matt Chapman (extreme flyball hitter) is a better hitter than Raimel Tapia (extreme groundball hitter.) But Vladimir Guerrero's still a better hitter than Cavan Biggio.
92-93 - Thursday, October 06 2022 @ 06:50 PM EDT (#423313) #
Speaking of Biggio, is there any reason for him to make the WC roster if Gurriel and Espinal are on it? Tapia would be your PH if Espinal starts vs. Ray, and based on recent usage Schneider wouldn't pull Merrifield for Biggio.
John Northey - Thursday, October 06 2022 @ 08:37 PM EDT (#423314) #
Most players, by their 3rd year in the majors, is pretty much what you see is what you get. They reached, the league adjusted, they adjusted back. Teoscar's 3rd year was 2020 when he had a 146 OPS+. His career (100+ games played) is 109-105-(146)-131-127 So we know his peak is the 120-140 range, his lower end is a 100-110 level. He is entering his age 30 season, his final before free agency. IMO the best move this post-season is to trade him to someone who needs a veteran presence in their OF. Who needs an RBI guy as odds are his value will just drop from now.

Soto - his 3rd year was also 2020 and he had a 217 OPS+ (higher than Judge this year) that year. Yikes. His career is 142-142-(217)-175-149 So his peak is in the 170-220 range, his floor is the 140's and he is entering his age 24 season next year so he has 6 years before he reaches Teoscar's age next season. Basically the worst one can expect from Soto is the best one should expect from Teoscar. At their floors Teoscar is a release candidate while Soto is still a solid DH.

All irrelevant as Soto isn't a free agent until after 2024. The Jays big question this winter is how to deal with the OF situation where 2 guys are 1 year away from free agency. Hernandez is turning 30 next season, Gurriel 29 (his OPS+ are 106-127-(138)-109-113 with the 1st 3 years all sub 100 games. IMO the Jays shouldn't resign either and let them head off to greener pastures unless they sign for $10 mil or less per year, max of 3 years and even then maybe just say 'nah'. Judge is the killer free agent this winter but odds are he'll give whoever signs him major regrets over that deal in a few years (entering age 31 season, just 3 times played 140+ games even if you count the minors). Next best OF free agent appears to be Brandon Nimmo (Mets, 5.0 bWAR, 130 OPS+ entering age 30 season, 274/367/433, a CF who also plays LF/RF, bats left) who looks to be a good fit actually. Overall though not much out there beyond SS and pitchers. I could see the Jays blowing a big wad on Verlander, while being in on DeGrom and Rodon as well hoping to get one of the 3 if they can't retain Stripling (much cheaper and might provide as much value as well in 2023-2025 for far far less with a far lower risk of injury). IMO the Jays should talk with Stripling's agent the minute the season is over and see if they can sign him to a 3 year deal at a low price (around what Kikuchi got - $12 per) before going nuts on the big guys.

In 2023 if all else stays the same (IE: no trades of starting players) the Jays are set at C/1B/SS/3B/CF/LF/RF with 3 decent options at 2B and Lopez in the wings. A big 3 in the rotation is set in Manoah/Gausman/Berrios even though Berrios had a bad year, and in the pen (Phelps the only one who is a free agent this winter). A few prospects are close - Moreno is ready, Lopez might be, Orelvis Martinez need to figure out how to cut his K's, Addison Barger made a major leap and could easily be ready by May, etc.). This team could be good for a long time, or a short time. But 2023 looks as good as 2022 and 2021 even if nothing is done. 2024...well...Chapman-Gurriel-Hernandez-Cimber are all free agents with Chapman the most important of the group.
John Northey - Thursday, October 06 2022 @ 08:58 PM EDT (#423315) #
Biggio is useful - as useful as anyone at the 25/26th man slot can be. I expect at least one of JBJ or Zimmer to be there no matter what, Espinal says he is fully ready to play and even as just a backup he is highly valuable due to defense. Gurriel is the tough choice - if he can only hit is he more useful than JBJ or Zimmer would be? Biggio healthy is > Gurriel PH only but is Gurriel PH more useful than Zimmer or JBJ defense/running? Hard to say. Who'd he hit for? Tapia late, JBJ or Zimmer in extras is about it. I'd have Gurriel rehab during round 1 and if he is ready for round 2 great if not then more rehab for round 3. Biggio takes walks, eats pitches, can fill in anywhere if needed and has power sometimes. Plus being a LH bat makes him a split up for the main lineup when used. All depends.

Seattle has 3 LH pitchers on their active roster - just 1 in the pen (Matthew Boyd), plus Ray & Gonzalez in the rotation so the value of Gurriel would be minimal. I expect Tapia to be in LF for the entire first round. FYI: Houston is even more RH with just 2 LH on their active roster Valdez (SP) and Will Smith (not that one). With the 3 batter minimum I have zero problem letting Tapia look foolish vs one of those guys then whoever is after him gets to hit vs a LHP or using Espinal or Merrifield to pinch hit for him.
uglyone - Thursday, October 06 2022 @ 11:11 PM EDT (#423317) #
So running up and down the numbers and up and down again.....

...Jays should win this series handily.


And when they do, I think I'll finally get excited about this team.
StephenT - Friday, October 07 2022 @ 01:08 AM EDT (#423318) #
fyi, here are the Jays and Mariners stats from my old scripts.
A.L. Run Environment ended up at about 4.27 runs per 9 innings:

 Runs Scored Per 9 IP   Runs Allowed Per 9 IP        Winning Percentage
( 1)   NYYankees 5.00 | ( 1)      Astros 3.23 | ( 1)      Astros 106-56  .654
( 2)    Bluejays 4.84 | ( 2)   NYYankees 3.52 | ( 2)   NYYankees  99-63  .611
( 3)        Rsox 4.62 | ( 3)        Rays 3.85 | ( 3)   Guardians  92-70  .568
( 4)      Astros 4.59 | ( 4)     Seattle 3.87 | ( 3)    Bluejays  92-70  .568
( 5)     Rangers 4.43 | ( 5)   Guardians 3.92 | ( 5)     Seattle  90-72  .556
( 6)   Minnesota 4.36 | ( 6)      Angels 4.19 | ( 6)        Rays  86-76  .531
( 7)   Guardians 4.31 | ( 7)    Bluejays 4.24 | ( 7)     Orioles  83-79  .512
( 8)     Seattle 4.29 | ( 8)   Minnesota 4.28 | ( 8)   White Sox  81-81  .500
( 9)   White Sox 4.26 | ( 9)     Orioles 4.32 | ( 9)        Rsox  78-84  .481
(10)     Orioles 4.24 | (10)   White Sox 4.46 | ( 9)   Minnesota  78-84  .481
(11)        Rays 4.18 | (11)      Tigers 4.52 | (11)      Angels  73-89  .451
(12)      Royals 4.07 | (12)     Rangers 4.66 | (12)     Rangers  68-94  .420
(13)      Angels 3.91 | (13)          As 4.86 | (13)      Tigers  66-96  .407
(14)          As 3.58 | (14)        Rsox 4.95 | (14)      Royals  65-97  .401
(15)      Tigers 3.53 | (15)      Royals 5.15 | (15)          As  60-102 .370
             Avg 4.28                Avg 4.26                   1217-1213     

An EqA of .260 is supposed to be league average.

                Age        EqA   BA  OBP  SLG  R27   ERP   R RBI HR  SB CS   PA
 George Springer          .301 .267 .339 .472  5.89   85  89  76 25  14  2  575
 Vladim Guerrero          .299 .274 .333 .480  5.75  103  90  97 32   8  3  699
  Alejandro Kirk          .298 .285 .371 .415  5.74   74  59  63 14   0  0  539
     Bo Bichette          .294 .290 .333 .468  5.54  100  91  93 24  13  8  697
 Teosc Hernandez          .294 .267 .316 .491  5.51   78  71  77 25   6  3  535
    Matt Chapman          .281 .229 .317 .433  4.92   79  83  76 27   2  2  615
 Lourdes Gurriel          .279 .291 .342 .400  4.84   60  52  52  5   3  4  489
 Santiag Espinal          .262 .267 .322 .370  4.17   54  51  51  7   6  6  488
    Raimel Tapia          .255 .265 .292 .380  3.86   45  47  52  7   8  2  428
         TORONTO          .283 .263 .326 .431  5.03  798 775 756 200 67 35 6114

                Age        EqA   BA  OBP  SLG  R27   ERP   R RBI HR  SB CS   PA
    Danny Jansen          .311 .260 .336 .516  6.35   39  34  44 15   1  0  247
  Gabriel Moreno          .279 .319 .356 .377  4.84    9  10   7  1   0  0   73
 Whit Merrifield          .278 .273 .315 .438  4.79   17  19  16  5   1  2  130
    Cavan Biggio          .260 .202 .317 .350  4.05   32  43  24  6   2  0  300
    Zack Collins          .251 .194 .256 .417  3.71    8   7  10  4   0  0   78
  Jackie Bradley          .201 .178 .250 .274  2.13    5   9   9  1   0  0   80
  Bradley Zimmer          .148 .101 .192 .213  1.00    3  14   5  2   3  2   99

                Age        EqA   BA  OBP  SLG  R27   ERP   R RBI HR  SB CS   PA
      Otto Lopez          .481 .667 .700 .667 18.90    3   0   3  0   0  1   10
    Gosuke Katoh          .287 .143 .400 .286  5.23    1   2   0  0   0  0   10
     Vinny Capra          .279 .200 .429 .200  4.87    1   2   0  0   0  0    7
  Tyler Heineman          .264 .286 .286 .429  4.22    2   2   1  0   0  0   14

REqA is based on Runs allowed,
CEqA is based on Component stats (H, BB, HR),
DEqA is Defense-independent (just K, BB, HR, rest normalized assuming .300 babip if I remember right):

                Age    W  L  ERA   REqA CEqA DEqA  dH   IP   H/9 BB/9 HR/9  K/9
     Alek Manoah      16  7  2.24  .211 .222 .253 -26 196.7  6.6  2.3   .7  8.2
  Ross Stripling      10  4  3.01  .235 .232 .249  -9 134.3  7.8  1.3   .8  7.4
   Kevin Gausman      12 10  3.35  .247 .260 .228  32 174.7  9.7  1.4   .8 10.6
    Jose Berrios      12  7  5.23  .286 .295 .284  15 172.0 10.4  2.4  1.5  7.8
    Hyun_jin Ryu       2  0  5.33  .292 .286 .292   0  27.0 10.3  1.3  1.7  5.3
   Yusei Kikuchi       6  7  5.19  .299 .300 .305  -1 100.7  8.3  5.2  2.1 11.1
     Mitch White       0  5  7.74  .331 .307 .265  11  43.0 12.3  3.3   .6  6.5
         TORONTO      92 70  3.90  .260 .263 .267    1441.3  8.5  2.6  1.1  8.7

                Age    W  L  ERA   REqA CEqA DEqA  dH   IP   H/9 BB/9 HR/9  K/9
    Anthony Bass       2  0  1.75  .183 .255 .288  -4  25.7  6.7  3.5  1.8  9.8
        Zach Pop       2  0  1.89  .189 .230 .245  -1  19.0  8.5   .9   .5  5.2
   Jordan Romano       5  4  2.11  .212 .214 .237  -6  64.0  6.0  3.0   .6 10.3
    David Phelps       0  2  2.83  .230 .244 .247  -1  63.7  7.4  4.4   .3  9.0
       Tim Mayza       8  1  3.33  .241 .250 .270  -4  48.7  7.8  2.2  1.3  8.1
     Adam Cimber      10  6  2.93  .243 .248 .255  -2  70.7  8.4  1.7   .8  7.4
     Yimi Garcia       4  5  3.10  .250 .235 .258  -6  61.0  7.1  2.4   .9  8.6
 Maximo Castillo       0  0  3.05  .252 .241 .284  -4  20.7  6.5  2.2  1.7  8.7
  Trent Thornton       0  2  4.11  .257 .263 .289  -6  46.0  7.8  3.3  1.4  7.2
 Trevor Richards       3  2  5.34  .294 .276 .271   3  64.0  8.0  4.9  1.3 11.5
 Ju Merryweather       0  3  6.75  .313 .294 .280   3  26.7 10.5  2.4  1.4  7.8

                Age    W  L  ERA   REqA CEqA DEqA  dH   IP   H/9 BB/9 HR/9  K/9
  Foster Griffin       0  0   .00  .000 .172 .229   0   2.0  4.5  4.5   .0  9.0
  Bowden Francis       0  0   .00  .000 .284 .154   0    .7 13.5   .0   .0 13.5
       Matt Gage       0  1  1.38  .219 .194 .268  -4  13.0  4.2  4.2   .7  8.3
   Anthony Banda       0  1  4.26  .261 .298 .279   1   6.3  9.9  4.3  1.4  9.9
     Anthony Kay       0  0  4.50  .266 .258 .200   1   2.0  9.0  4.5   .0 13.5
  Jeremy Beasley       0  0  4.80  .287 .296 .304   0  15.0  8.4  3.0  2.4 11.4
     Sergio Romo       0  1  4.91  .325 .215 .343  -2   3.7  2.5  4.9  2.5  7.4
  Casey Lawrence       0  1  7.50  .327 .322 .323   0  18.0 11.5  2.0  2.5  5.5
  Andrew Vasquez       0  0  8.10  .337 .301 .314   0   6.7  8.1  4.1  1.4  8.1
    Ryan Borucki       0  0  9.95  .366 .346 .340   0   6.3  9.9  7.1  2.8 11.4
  Tayler Saucedo       0  0 13.50  .414 .480 .498   0   2.7 20.3  3.4 10.1   .0
 Whit Merrifield       0  0 18.00  .464 .475 .504   0   1.0 18.0   .0  9.0   .0
  Shaun Anderson       0  0 18.00  .464 .470 .255   2   1.0 36.0   .0   .0   .0
    Thomas Hatch       0  1 19.29  .477 .466 .385   4   4.7 23.1  3.9  5.8  7.7

----
I've no idea which Mariners are currently on the roster.
These stats just include their time with the Mariners:

                Age        EqA   BA  OBP  SLG  R27   ERP   R RBI HR  SB CS   PA
 Julio Rodriguez          .315 .284 .340 .507  6.29   90  84  74 28  25  7  556
  Eugenio Suarez          .296 .236 .326 .459  5.38   86  76  87 31   0  0  622
       Ty France          .291 .276 .329 .437  5.17   79  65  84 20   0  0  602
     Cal Raleigh          .286 .211 .282 .489  4.95   56  46  63 27   1  0  411
    Jesse Winker          .273 .219 .342 .344  4.42   60  51  53 14   0  0  544
   J.P. Crawford          .267 .243 .337 .336  4.18   63  57  42  6   3  2  597
  Carlos Santana          .265 .192 .288 .400  4.08   32  35  39 15   0  0  292
    Adam Frazier          .242 .238 .300 .311  3.26   52  61  42  3  11  6  596
    Abraham Toro          .219 .185 .239 .324  2.54   26  36  35 10   2  0  351

                Age        EqA   BA  OBP  SLG  R27   ERP   R RBI HR  SB CS   PA
     Dylan Moore          .296 .224 .360 .385  5.40   35  41  24  6  21  8  250
    Sam Haggerty          .289 .256 .325 .403  5.09   26  29  23  5  13  1  197
   Mitch Haniger          .280 .246 .308 .429  4.69   30  31  34 11   0  0  247
 Taylor Trammell          .264 .196 .287 .402  4.07   13  15  10  4   2  1  115
    Luis Torrens          .229 .225 .285 .298  2.85   13  13  15  3   0  0  165
  Jarred Kelenic          .210 .141 .221 .313  2.30   13  20  17  7   5  2  181
             SEA          .271 .230 .310 .389  4.35  696 690 663 197 83 27 6056

                Age        EqA   BA  OBP  SLG  R27   ERP   R RBI HR  SB CS   PA
   Brian O'Keefe          .348 .333 .500 .333  8.10    1   0   0  0   0  0    4
      Tom Murphy          .325 .273 .415 .424  6.84    6   9   1  1   0  0   41
      Drew Ellis          .257 .333 .333 .333  3.79    0   0   0  0   0  0    3
       Mike Ford          .252 .172 .368 .207  3.61    3   1   3  0   0  0   38
   Marcus Wilson          .225 .200 .333 .200  2.71    0   1   0  0   0  0    6
     Kevin Padlo          .225 .200 .273 .300  2.71    1   0   3  0   0  0   11
     Curt Casali          .213 .125 .286 .225  2.37    3   7   3  1   0  0   49
       Jake Lamb          .211 .167 .242 .300  2.32    2   3   2  1   0  0   33
      Kyle Lewis          .198 .143 .213 .304  1.99    4   6   5  3   0  0   61
    Justin Upton          .174 .125 .236 .208  1.42    2   2   3  1   0  0   55
  Donovan Walton          .000 .000 .000 .000   .00    0   1   0  0   0  0    0
    Steven Souza         -.088 .158 .158 .158  -.26    0   0   1  0   0  0   19
 Stuar Fairchild         -.224 .000 .000 .000 -2.69    0   0   0  0   0  0    3
    Andrew Knapp         -.224 .000 .000 .000 -2.69    0   0   0  0   0  0    4
     Jack Larsen         -.224 .000 .000 .000 -2.69    0   0   0  0   0  0    1
 Travi Jankowski         -.224 .000 .000 .000 -2.69    0   0   0  0   0  0    1

                Age    W  L  ERA   REqA CEqA DEqA  dH   IP   H/9 BB/9 HR/9  K/9
   Logan Gilbert      13  6  3.25  .243 .256 .262  -2 185.7  8.2  2.4   .9  8.4
   Luis Castillo       4  2  3.17  .247 .246 .245   1  65.3  7.6  2.3   .8 10.6
    George Kirby       8  5  3.39  .252 .263 .249  12 130.0  9.2  1.5   .9  9.2
      Robbie Ray      12 12  3.71  .253 .268 .280  -8 189.0  7.8  3.0  1.5 10.1
    Chris Flexen       8  9  3.73  .258 .273 .289 -10 137.7  8.6  3.3  1.1  6.2
  Marco Gonzales      10 15  4.13  .277 .287 .302 -12 183.0  9.5  2.5  1.5  5.1
             SEA      90 72  3.60  .255 .260 .273    1447.0  7.9  2.8  1.2  8.7

                Age    W  L  ERA   REqA CEqA DEqA  dH   IP   H/9 BB/9 HR/9  K/9
    Erik Swanson       3  1  1.68  .190 .206 .209   0  53.7  6.5  1.7   .5 11.7
    Andres Munoz       2  5  2.49  .223 .207 .213  -1  65.0  6.0  2.1   .7 13.3
     Paul Sewald       5  4  2.67  .229 .205 .274 -18  64.0  4.5  2.4  1.4 10.1
     Penn Murfee       4  0  2.99  .230 .221 .251  -8  69.3  6.2  2.3   .9  9.9
    Ryan Borucki       2  0  4.26  .265 .281 .316  -3  19.0  8.1  2.8  1.9  6.2
      Matt Festa       2  0  4.17  .267 .260 .280  -4  54.0  7.2  3.0  1.7 10.7
      Matt Brash       4  5  4.44  .269 .274 .257   5  50.7  8.2  5.9   .5 11.0
  Diego Castillo       7  3  3.64  .270 .239 .266  -7  54.3  6.6  3.6   .8  8.8

                Age    W  L  ERA   REqA CEqA DEqA  dH   IP   H/9 BB/9 HR/9  K/9
   Riley O'Brien       0  0   .00  .000 .298 .274   0   1.0  9.0  9.0   .0  9.0
       Ken Giles       0  0   .00  .000 .168 .245  -1   4.3  2.1  8.3   .0 12.5
    Matthew Boyd       2  0  1.35  .167 .169 .250  -4  13.3  3.4  5.4   .0  8.8
    Roenis Elias       0  0  3.52  .246 .269 .287  -1   7.7  8.2  3.5  1.2  7.0
 Justu Sheffield       1  0  3.86  .255 .257 .298  -2  11.7  6.9  4.6   .8  5.4
     Wyatt Mills       0  0  4.15  .262 .188 .250  -2   8.7  5.2  3.1   .0  6.2
 Antho Misiewicz       0  1  4.61  .274 .276 .282   0  13.7  9.2  4.0   .7  5.3
    Tommy Milone       1  1  5.40  .291 .279 .345  -6  16.7  7.6  3.2  2.2  2.7
 Dr Steckenrider       0  2  5.65  .297 .329 .289   4  14.3 13.2  3.1  1.3  6.3
     Danny Young       0  0  7.36  .330 .392 .303   3   3.7 17.2  4.9  2.5 12.3
   Yohan Ramirez       1  0  7.56  .333 .335 .365  -1   8.3  7.6  6.5  3.2 10.8
     Sergio Romo       0  0  8.16  .344 .345 .358  -1  14.3 11.3  2.5  3.8  6.9
       Matt Koch       0  0  8.31  .346 .339 .372  -1   4.3 10.4  2.1  4.2  6.2
    Luis Torrens       1  0  4.50  .357 .363 .394   0   2.0 13.5   .0  4.5   .0
 Bren Bernardino       0  1  3.86  .395 .319 .314   0   2.3 11.6  7.7   .0   .0

fyi, the park factors used are at least a few years old, no idea if they're still accurate:

 Park Factors ((PF-1)*100, hitters' parks first):
    1    1    3    4    4    6    7    8    9   10   11   11   13   13   15
   TEX  CHW  BOS  BAL  NYY  DET  KAN  TOR  ANA  MIN  HOU  OAK  CLE  TAM  SEA
   5.5  5.5  4.0  3.0  3.0  1.5   .5 -1.0 -1.5 -2.0 -2.5 -2.5 -4.0 -4.0 -5.0
The raw data was taken from http://www.dougstats.com/2022.html .
smyttysmullet94 - Friday, October 07 2022 @ 09:45 AM EDT (#423319) #
This is so interesting. Thank you, Magpie.
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