Trick question. Think outside the box.
I don't want to give it away but don't answer with the obvious Sandy Koufax.
OT: Baseball Triva
- Thread starter colbert17
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I don't want to give it away but don't answer with the obvious Sandy Koufax.
I know the answer but like you said, people need to look outside the box. Hint 3. He was an LA player.
I think it's where I remember the answer. Rookie. Got his no-no in his 4th or 5th start.
I certainly understand why a Padres fan like you would think Soto is overrated. But he's in pretty good company on your 20/80 question, so maybe he's better than you give him credit for.
Soto is an analytics dream. Mostly because he walks all the time. He probably walks too much. He takes borderline pitches that he needs to swing at in clutch situations. I watch at least 155 Padre games a year and you have to see him every day to see what I mean.
Is he a good player? Yes. Is he the generational type player some people have called him? No way.
This an issue I have with on-base-percentage. It treats a walk as being as good a hit. Sometimes that's true but not when there are runners in scoring position, two out, and a weaker hitter behind you. I understand the criticism of batting average -- it treats a walk as nothing, although at least it treats a walk better than an out. But equating a walk to a hit is not a perfect answer.
Totally agree; and though not really the case with Soto, many of the guys who rack up walks also end up with a really high amount of strikeouts because they’re going deep in so many counts; i haven’t seen stats to back it up, but I would bet rates of runners stranded in scoring position have increased over the last generation.
Yasmani Grandal and Yoan Moncada (when these guys are actually healthy) have killed me with this in recent years as a White Sox fan. RISP, and they’re taking pitches like crazy, working the count full, then they either walk or strikeout.
SLG% has the same weakness by the way. It rewards the guy who picks up a homer and three strikeouts in four at bats (slg 1.00) over the guy with two singles, two stolen bases, and two outs in play (slg .5) that maybe advanced runners.
I think these stats are valuable, but the last generation of front offices turned OPS components into the metric that defines the goal of the game instead of a metric for consideration in evaluating a player’s ability to play the game.
On the pitching side it’s spin rate and velocity. I’m sure overall those metrics correlate to success, but Greg Maddux showed that you can dominate in the most hitter friendly era by changing speeds and locating your pitches. If I were a value-inclined front office, I’d be looking for those guys because they’re not blowing up the statcast charts, and can probably be had at a discount.
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All of the current sports have been somewhat damaged by analytics and optimization efforts. That sounds like an old man statement, but the problem isn’t the analytics themselves, it’s that if you’re doing the same thing as 29 other teams, and half of them do it better than you…you’re not actually taking the smartest approach to winning. The lack of originality in front office approaches and management styles, and ubiquity in styles of play makes the game less interesting, and dooms at least half the teams at the start of the season (because they’re trying to win via an approach that they’re not the best at).
And the ‘smartest’ or most efficient approach to winning, isn’t the only way to win. The 2015 Royals were last in walks in the AL and second to last in home runs. They won the World Series. And as a pitching staff they were 11th (out of 15) in strikeouts. These are the “three true outcome” stats that statistical analysis of the game loves, and the Royals were bottom third in all three. Using pre-moneyball stats: they were third in ERA, second in batting average, third in stolen bases, and on the batting side they struck out the least.
Unfortunately, most front offices would rather suck trying to play by the new conventional wisdom than risk failure by allocating resources to an approach that’s considered outdated.
And regarding moneyball, its disciples forget that Bean was trying to find value. If other teams are pricing walks higher, then the value in that stat is lost. Now you’re just overpaying for an outcome that is worse than a hit.
And the ‘smartest’ or most efficient approach to winning, isn’t the only way to win. The 2015 Royals were last in walks in the AL and second to last in home runs. They won the World Series. And as a pitching staff they were 11th (out of 15) in strikeouts. These are the “three true outcome” stats that statistical analysis of the game loves, and the Royals were bottom third in all three. Using pre-moneyball stats: they were third in ERA, second in batting average, third in stolen bases, and on the batting side they struck out the least.
Unfortunately, most front offices would rather suck trying to play by the new conventional wisdom than risk failure by allocating resources to an approach that’s considered outdated.
And regarding moneyball, its disciples forget that Bean was trying to find value. If other teams are pricing walks higher, then the value in that stat is lost. Now you’re just overpaying for an outcome that is worse than a hit.
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Re the Royals or other WS winners, the ‘80 hockey team, Buster Douglas, etc….why would prior metrics always match real time performance? They are just averages. The best on one day or week aren’t always the best the week, month or season before.
Agreed, sometimes a team catches lightning in a bottle. However, over the course of a season (vs a boxing match) results will skew towards the teams that are very good at something. The issue for me is that analytics has all of the teams trying to be good at the same thing.
If every team in basketball is launching threes for example, naturally the teams that are best at that will win the most.
Some would argue: why try to win any other way when the three point shot is the most efficient play in basketball? The home run most efficient in baseball? Etc
My answer would be that if other teams are significantly better at shooting threes or home runs, why would I try to be 10th or 20th best at it? Why not try something else that exploits their weakness?
(For example, although teams haven’t really started to return to a ground and pound game in football, I heard a stat last season that yards per carry up the middle were trending higher because teams aren’t signing big run stuff DTs in an era of pass offenses. Sure the passing attack is more efficient, but more teams that lack franchise QBs should be exploiting the relative cheapness of run blocking guards and RBs to push a dominating ground game that benefits from an era of defenses trying to protect against the air attack)
Because weaknesses are factored into the metrics too. If metrics say it's better to swing for the fences (hit HRs) and your lineup is only 10th or 20th best at it, opting to play small ball instead is a disadvantage. That's baked into the metrics.
Metrics say it wouldn't help you to shoot more 2s. Instead, focus on getting better at 3s.
The best militaries in the world have more fighter jets. Rather than increase jet spending to reach only #20 in jet counts, lets ramp up production instead on propeller aircraft !
You see, the favored approach is that for a reason. Metrics say it doesn't help to pursue a weaker strategy.
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There’s two issues that come to mind:
1) the metrics can become a self-fulfilling prophecy as more teams adhere to them. For example: slugging percentage guides on maximizing bases per at bat. Let’s say teams believe that is the best method to victory, and then most of them pursue that angle in constructing their rosters. The teams that do it best are the ones who ultimately succeed because everyone is doing it, and at the end of the year the records will give your “proof” that slugging percentage is correlated with winning. However, that’s only the final result because the teams (in this example) turned the league into a slugging % contest by all pursuing that metric in roster construction. In other words, if teams believe that three pointers are how you win in basketball, and they all try to win that way, the teams who do it best will win thereby proving that being the best at three pointers wins.
That’s not to say I doubt the validity of these statistics. The importance of extra-base hits and three points is undeniable. That leads to point 2
2) You can’t always just “get better” at it. You either have the horses or you don’t, and if you don’t, you have to buy them, and because the leagues are becoming homogeneous in style and philosophy, the dudes who are masters of that style (the high OPS guys in baseball, the long slashers with a great outside jumper in hoops, the franchise QBs in football) will command the most resources and can’t be easily bought by a lot of franchises. If you can’t be the best at it with your current roster, and you don’t have the resources to attract free agents…then conventional wisdom says you have to tank to win the high draft pick.
3) The alternative is to find other ways to win. You don’t hire a front office full of analytics guys to go against the analytics, but I believe that’s what you have to do to extract value and win if you’re not positioned to win via the preferred methodology (and you can still use analytics for things like defensive positioning and in-game decision making). Also “analytics” or “metrics” are becoming synonymous with certain stats, but ERA is a metric. So it’s not about tossing analytics / metrics together, it’s about deviating from what’s popular to find value and alternative routes to competitiveness.
The final challenge here goes back to free agency. The best way to beat the shift in baseball is to hit singles the other way. If you’re a left-handed doubles / home run type hitter, you’re not going to give up your potential for pull power to settle for a single (which isn’t really OPS accretive)…it would risk your value even if it means sacrificing batting average because everything you hit on the ground is going into the shift. So I think player marketability (the players desire to put up stats that the market rewards) further reinforces this cycle of playing the game a certain way even if it starts to become a less optimal play.
I could go on and on about this 😂
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I’m responding again to address this point in detail.
What am I advising here is not the equivalent of spending on propeller aircraft, because ultimately that’s still playing the same style of game (air warfare) but doing it worse than your opponent.
What I’m proposing would be to find a style of warfare that does not try to compete for aerial supremacy—assuming you can never be close to on the level as your opponent—and puts those resources into something else. I’m not a war tactician so can’t come up with an exact comparison, but we can agree in Afghanistan, Iraq, Vietnam, the US had certain advantages and the opposition had to compete using an entirely different method of fighting to turn those into decade-long slogs.
Obviously in sports you’re confined to the rules of the game, but you can still be good at things the other teams are overlooking (and you can usually do it at a discount) or perhaps even exploit weaknesses the new conventional wisdom is creating (see my earlier point about defensive tackles and yards to be had up the middle).
That's Soto. He drives in runs but mostly at meaningless times in games.
Plus he's an absolute butcher in LF
At the end of the day, every instance (every AB, every running play, etc) has a positive or negative outcome. Metrics tell you the percentages of each outcome.
You are suggesting to try other things that metrics doesn't suggest is the optimal approach. 'Exploiting weaknesses' is already factored into the math.
You are suggesting to try other things that metrics doesn't suggest is the optimal approach. 'Exploiting weaknesses' is already factored into the math.
In his career or this year? His clutch stats this year look pretty good to me.
| Split | G | PA | AB | R | H | 2B | 3B | HR | RBI | SB | CS | BB | SO | BA | OBP | SLG | OPS | TB | GDP | HBP | SH | SF | IBB | ROE | BAbip | tOPS+ | sOPS+ |
| 2 outs, RISP | 38 | 46 | 34 | 15 | 10 | 2 | 0 | 1 | 14 | 1 | 0 | 12 | 7 | 0.294 | 0.478 | 0.441 | 0.92 | 15 | 0 | 0 | 0 | 0 | 4 | 1 | 0.346 | 104 | 152 |
| Late & Close | 47 | 62 | 47 | 8 | 12 | 2 | 0 | 2 | 5 | 3 | 0 | 14 | 13 | 0.255 | 0.419 | 0.426 | 0.845 | 20 | 0 | 0 | 0 | 1 | 4 | 0 | 0.303 | 87 | 147 |
| Tie Game | 83 | 135 | 103 | 17 | 28 | 7 | 0 | 9 | 22 | 3 | 0 | 29 | 31 | 0.272 | 0.43 | 0.602 | 1.032 | 62 | 3 | 1 | 0 | 2 | 5 | 1 | 0.292 | 126 | 183 |
| Within 1 R | 91 | 220 | 174 | 27 | 45 | 13 | 0 | 10 | 27 | 4 | 3 | 42 | 55 | 0.259 | 0.4 | 0.506 | 0.906 | 88 | 3 | 1 | 0 | 3 | 5 | 1 | 0.313 | 99 | 148 |
| Within 2 R | 94 | 302 | 236 | 35 | 58 | 16 | 0 | 11 | 32 | 5 | 3 | 62 | 69 | 0.246 | 0.401 | 0.453 | 0.854 | 107 | 5 | 1 | 0 | 3 | 5 | 1 | 0.296 | 89 | 137 |
| Within 3 R | 94 | 353 | 275 | 43 | 69 | 18 | 0 | 15 | 39 | 6 | 3 | 74 | 78 | 0.251 | 0.408 | 0.48 | 0.888 | 132 | 5 | 1 | 0 | 3 | 7 | 1 | 0.292 | 96 | 145 |
| Within 4 R | 96 | 375 | 290 | 47 | 74 | 18 | 0 | 15 | 40 | 6 | 3 | 81 | 79 | 0.255 | 0.416 | 0.472 | 0.888 | 137 | 5 | 1 | 0 | 3 | 7 | 1 | 0.297 | 96 | 146 |
| Margin > 4 R | 31 | 50 | 40 | 8 | 13 | 5 | 0 | 2 | 14 | 0 | 0 | 9 | 7 | 0.325 | 0.44 | 0.6 | 1.04 | 24 | 1 | 0 | 0 | 1 | 1 | 1 | 0.344 | 128 | 177 |
| Ahead | 58 | 164 | 126 | 26 | 34 | 11 | 0 | 5 | 21 | 1 | 1 | 37 | 33 | 0.27 | 0.433 | 0.476 | 0.909 | 60 | 1 | 0 | 0 | 1 | 3 | 1 | 0.326 | 101 | 147 |
| Behind | 55 | 126 | 101 | 12 | 25 | 5 | 0 | 3 | 11 | 2 | 2 | 24 | 22 | 0.248 | 0.389 | 0.386 | 0.775 | 39 | 2 | 0 | 0 | 1 | 0 | 0 | 0.286 | 72 | 118 |
Yes I totally believe that’s true regarding outcomes. And I’m not anti-analytics as stated earlier in this thread. If I had to win a baseball game, and had infinite resources at my disposal to do so, I’d be layering OPS, exit velo hitters and high velo, high spin rate, high k rate pitchers. I believe the analytics when they say that would give me the best chance of success to win.
However, to your second point, we’re not talking about card games. All else being equal, your chance of drawing an ace from a deck of cards was the same 100 years ago as it is today. Backward looking is forward looking so long as the card dynamics remain the same. Not true in sports.
This is where the self-fulfilling prophecy comes in: metrics suggested station to station, home run dependent baseball 20 years ago based on the probability of scoring and frequency of home runs, therefore teams ran less while further increasing home run % and carried fewer players with speed / base running in their core skill sets…all of which only reinforces the statistical disadvantage to stealing bases. However, outside of the stole base data set is the reality that pitchers are tending to throw more breaking pitches than before, are employing max effort (often slowing down deliveries as they torque up for velocity), and so there may be significant upside vs the statistical probabilities if you can exploit these changes to the game with skilled base runners. And of course now with the bigger bases teams are taking it into consideration.
Because the game is dynamic the reality on the field of play does not have to mirror the statistical probabilities; tomorrow isn’t bound to be the same as yesterday the odds will change according to the gameplay…unlike a card game where the probabilities are set in stone.
I did not know that Soto is a poor fielder. As you can see from the link, that was a problem at Washington. And, as you can also see from the link, lack of effort is a big part of the problem. https://www.talknats.com/2022/05/24/time-move-juan-soto-back-lf/
What's considered the best medical treatment today may not be the best tomorrow, as more research is done and advances are discovered. Maybe someday all cancer will be curable. If you are saying that as different strategies are tried and new data are compiled, perhaps new strategies will prove best. Sure, of course. It's not a card game. Probabilities aren't fixed. Fences are moved in and out. Balls are juiced, then not juiced. Rules are changed.
If a team wants to deviate from metrics, attempting a new approach, perhaps something different will be discovered best. Existing data show otherwise, but as more data come in, who knows. But by not following the herd and existing data, you're likely hurting yourself.
Jumping around your post a little bit:
You are hurting yourself when you are playing the game the way the metrics suggest to play it, but you are worse than your opponents at playing that game. That is the opposite of good strategy. Going back to the beginning, that is my main point here and my biggest gripe with metrics-based approaches to sports today.
When it comes to the forward-looking blind spots in the metrics, I’m not talking about their inability to pick up on new developments or major changes to the sport (like rule changes), or even talking about new “discoveries.” It’s as simple as someone saying (pre-rule change) “hmm…pitchers are throwing a ton of sliders these days, maybe we can pick up extra bags with more speed on the roster — sure the data says SBs don’t add marginal runs, but that’s based on data from a generation of rosters composed of players built for station to station baseball”
At the end of the day you have to be good at something to win. And doing something that is statistically unfavorable, but doing it well, can be better than doing what is statistically favorable but doing it worse than your opponent — who is doing the exact same thing but better.
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generally I would say no, that last part is not correct. again, that's all factored into the metrics if they are advanced enough. metrics are more than everybody's collective average....they are the past outcome probabilities of the specific player in the specific scenario as close as can be approximated.
Metrics will not suggest a player with no career home runs but good bat control should swing for the fences with no outs and a runner on second. Metrics will say bunt or hit the other way.
Perhaps we’re just talking about two different approaches of analytics. Play to play, of course you should align your defense where the guy is most likely to hit the ball. You should pitch where the opponent doesn’t hit well, etc.
I’m talking about the broader use of analytics to define how teams should be constructed and their general philosophy or approach to the game.
I have a lot of sympathy for your dislike of analytics. I just want to point out one thing. You say (correctly) that these days everyone uses analytics. But even before there were analytics, everyone used the same tools (ERA, batting average, RBIs); it's not as though some organizations used different evaluation methods as others.
I'm sure you know the saying that it's not the X's and the O's, but the Jimmies and the Joes. I think baseball is the same. While strategy -- the baseball equivalent of the X's and the O's -- is important, the key is to judge talent correctly and to coach it well. I don't know if you are a Phillies fan, but these traditionally have been Philly weaknesses.
Yes, agree with your point about the stats that used to dominate the thinking of the game. It’s not that I am a fan of the old stats, I had no problem with their demise, it’s that all metrics should be tools for understanding performance and not rigid dogma. Humans are very good at applying blind faith in statistical methodologies to their own detriment.
My own casual observation (and perhaps it’s inaccurate) is that styles of play are more homogenous across sports today. In baseball i feel like I see a lot of teams with similar lineups, similar platoons situations, similar approaches to managing pitchers, pitching staffs dominated by hard sliders and sweepers. Just seems like there is less character to individual clubs (and I’m not an old guy, so I’m comparing now to 20-25 years ago). I’m also still religiously devoted to the game.
I’m a White Sox fan — no stranger to bad baseball.
Agree on the walking idea.
Soto may never repeat his performance from the young star WS year, but until he gets x older (30?), he is going to have a good rep, and there is going to be the hope that he can regain it.
Understandable that not having him do (or having done) that while on “your” team leaves a what could be feeling.
Still no. Now you’re back to square 1. It’s already established that homers win so there’s no model that wins with small ball.
Assuming modern metrics show that starters should go 5, middle guys 3 and closer 1 (to simplify). No model will show it’s better to have your starters go 8.
Two thousand years of chess models show it’s better to have a queen and rook in the end game. No other model will show its better to have a bishop and two pawns.
Get it ? If home runs are proven more correlated to winning than singles and batting avg. then no, no model will simulate a roster constructed with singles hitters that is even more correlated with winning.
Explained another way. The optimally-designed small ball team would not be likely to finish higher in the standings than the optimally designed home run hitting team, so it’s not a strategy to follow.
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My point was never that the home run is a suboptimal play or that the metrics are wrong. I repeatedly said I would follow all of the metrics if I had to win a game and had infinite resources.
My point is in your last paragraph. Not that I’d rather be an optimal small ball team than an optimal home run team, but I’d rather be an optimal small ball team than a suboptimal home run team in a league congested with more optimized home run teams.
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