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Old 05-18-2016, 02:36 PM   #1
sethtearz
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So Trout Kinda Sucks?

Has Mike Trout disappointed for anyone else?

I'm now in 2018 in my season. He is 26-years-old.

2016: .256/.361/.479, 30 HR in 158 games
2017: .263/.369/.487, 30 HR in 158 games
2018: .300/.382/.533, 2 HR in 8 games

Now, he's off to a good start offensively in 2018, but his defense in CF is down to a 56, and his speed is down to a 50 (on scale of 1-100).

Last edited by sethtearz; 05-18-2016 at 02:38 PM. Reason: clarification
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Old 05-18-2016, 02:46 PM   #2
Assos
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Take in mind the computer has a bell curve when dealing with players that do not have careers in the future. Centerfielders have been known historical to age faster then other players because of the wear and tear on the body. Look at Josh Hamilton or Curtis Ganderson as examples that are recent.
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Old 05-18-2016, 02:52 PM   #3
sethtearz
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Quote:
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Take in mind the computer has a bell curve when dealing with players that do not have careers in the future. Centerfielders have been known historical to age faster then other players because of the wear and tear on the body. Look at Josh Hamilton or Curtis Ganderson as examples that are recent.
I get it, but disappointed it's happened this early with Trout. Again, he's 26 in the game. He isn't 29.
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Old 05-18-2016, 03:29 PM   #4
Number4
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It happens. In another simulation he might kick it up (another) notch.

Think about moving him to a corner outfield position, where he can be a plus defender instead of "only" average in CF.
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Old 05-18-2016, 03:35 PM   #5
sethtearz
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It happens. In another simulation he might kick it up (another) notch.

Think about moving him to a corner outfield position, where he can be a plus defender instead of "only" average in CF.
He's a 31 in LF. So I'd just have to ride that out, hope he improves that LF pretty quickly?
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Old 05-18-2016, 03:36 PM   #6
Assos
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31 is passable for a LF as long as he out hits his defense.
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Old 05-18-2016, 03:50 PM   #7
TomVeal
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Your version of Trout should improve fairly quickly in LF. His low rating is just the result of inexperience at the (easily learned) position.

The OOTP future is unpredictable (like every other future). In a league that I'm in, a first baseman named Jordan Steranka, who was cut by Bradenton in real life, is currently (2024) one of the Pirates' top players. OTOH, Andrew McCutchen declined rapidly after the 2016 season. Now (admittedly, he's 37) he starts only against lefties and is rated 25 at CF.
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Old 05-18-2016, 04:04 PM   #8
grmagne
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Your version of Trout should improve fairly quickly in LF. His low rating is just the result of inexperience at the (easily learned) position.

The OOTP future is unpredictable (like every other future). In a league that I'm in, a first baseman named Jordan Steranka, who was cut by Bradenton in real life, is currently (2024) one of the Pirates' top players. OTOH, Andrew McCutchen declined rapidly after the 2016 season. Now (admittedly, he's 37) he starts only against lefties and is rated 25 at CF.


Exactly. I had a league where Rafael Devers was still an excellent everyday player at age 40. But other people probably saw him turn into a bust in their league. There's a lot of randomness in how players develop.
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Old 05-18-2016, 04:05 PM   #9
Hammercraft
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It depends on a lot of things once the OOTP engine sims future outcomes. I've run the future 5 years a bunch of times in different ways, sometimes Trout is awesome and sometimes he starts to drop off at 30 or so, but he never completely sucks. Here's his current iteration playing for the relocated Austin Angels, still playing CF (realigned MLB):

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Old 05-18-2016, 04:24 PM   #10
Number4
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Trout would learn LF quickly - especially in spring training. Might let him play the whole spring there and he'll be ready for sure.
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Old 05-18-2016, 04:46 PM   #11
MrWideFrame
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Trout would learn LF quickly - especially in spring training. Might let him play the whole spring there and he'll be ready for sure.
Yeah he'll be a 90 there by season's end easily.
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Old 05-18-2016, 05:26 PM   #12
Lukas Berger
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Quote:
Originally Posted by sethtearz View Post
I get it, but disappointed it's happened this early with Trout. Again, he's 26 in the game. He isn't 29.
Quote:
Originally Posted by Number4 View Post
It happens. In another simulation he might kick it up (another) notch.

Think about moving him to a corner outfield position, where he can be a plus defender instead of "only" average in CF.
Quote:
Originally Posted by grmagne View Post
Exactly. I had a league where Rafael Devers was still an excellent everyday player at age 40. But other people probably saw him turn into a bust in their league. There's a lot of randomness in how players develop.
As the last two quoted comments allude to, there's a lot of randomness involved. The next play-through can and likely will be be totally different
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Old 05-18-2016, 05:28 PM   #13
Lukas Berger
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Generally Trout will be a stud for a long, long time. There will be the occasional game where he turns into Bernie Carbo or something. It happens
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Old 05-18-2016, 05:34 PM   #14
NoOne
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Originally Posted by Assos View Post
Take in mind the computer has a bell curve when dealing with players that do not have careers in the future. Centerfielders have been known historical to age faster then other players because of the wear and tear on the body. Look at Josh Hamilton or Curtis Ganderson as examples that are recent.
First response about OP:

mike trout is a monster most of the time in ootp. not much can be said about his stats in your particular league without understanding the baseline environment in that league.

hof'ers can have bad years... you may just be unlucky in having a few to start his career, causing a little baseless concern. or maybe the teams you play in the season has more upper-echelon pitchers than other divisions schedules etc etc... so much to look at to know or at least try to understand.

56 defense in CF isn't bad for an offensive CFer like mike trout, but you should definitely get him into LF sooner than later. he's just an average CF'er in real life and the game - simple as that.

2012: 13.3 UZR, 21 DRS, -3.8 ARM, 16.7 RngR, 0.4 ErrR
2013: 4.4 UZR, -9 DRS, -1.7 ARM, 5.6 RngR, 0.5 ErrR
2014: -9.8 UZR, -9 DRS, -5.1 ARM, -5.0 RngR, 0.3 ErrR

*** 2015 was ~1/2 done
2015: -3.1 UZR/150, 3 DRS, -1.6 ARM, -0.3 RngR, 0.9 ErrR

"One of these things is not like the others, One of these things just doesn't belong, Can you tell which thing is not like the others. By the time I finish my song?" - sesame street

... namely his rookie year results. he made a good first impression and people hang on to it as if their lives depend upon it.

(from: Mike Trout: Why do defensive metrics hate Angels superstar?

i have very little respect for these stats when used for very specific things or comparing two players in an absolute way... they are in general at least in the ballpark, though.

it's difficult to find anything that shows he is anything but an average overall CF. which is likely an above average++ LF.

-------------------

CF defintitely detoriates faster than other positions in ootp. i have no idea what hte baseline is so i cannot comment on its rate. he's going to be a left fielder in real life sooner than later, too. only ego would keep him in CF. all modern defensive metrics are junk relative to quantifying range... so far he's really just an average CF, but when a superstar bat plays ss/cf, fans project super abilities to all aspects of their game if just average at it.

he made some amazing plays his rookie year too... some people get hung up on 1 or 2 eye-catching events as opposed to the big picture. - think tim salmon.

although i don't get your bell-curve comment. the player's ratings may be created in a different way, but the game engine still inputs the ratings and outputs the statistics in the exact same way. if he's near the top ratings wise, he should be near the top for statistical results too. the only thing you can do is manipulate LTM to shift that bell curve around a different mean, but it's always a similar curve each year barring an insane amount of outlier performances by individual players.

Last edited by NoOne; 05-18-2016 at 05:40 PM.
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Old 05-18-2016, 05:48 PM   #15
Number4
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Don't get me wrong, he can play center field. However I think that I'd rather have a good defender there then just average, and if you have a good corner infielder that can play center, you have more roster flexibility - corner outfielders are way easier to come by then CF, so if your first choice CF gets hurt, Trout moves to center and your 4th outfielder to left.

It depends on who is available in your organization, I'd say: if you aquire another outfielder, it should be a center fielder preferably, and let Trout play the next spring in left - even if he'll start the season in center, somewhere in his career will the point come when he has to move to left, so give him experience there.
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Old 05-18-2016, 06:12 PM   #16
grmagne
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I checked out a random sample of young prospects/MLB stars to see how they turned out in my last simulation. Most of them had good careers, with a few notable exceptions (I'm looking at you Vlad, Jr.) Mike Trout had a HoF career where he was an elite player with the Angels until age 34, OK with the Rangers until age 36, and then, strangely, a Single-A minor leaguer for the Phillies at age 39. My table below only tracks MLB stats, so his final season is listed as age 37.


If anyone wants to compare the same group of players that I did, you can copy and paste this SQL into Microsoft Access after importing these csv tables: players, teams, players_career_batting_stats

SELECT players_career_batting_stats.player_id, players.first_name, players.last_name, Min(players_career_batting_stats.Year) AS [Debut Year], Max(players_career_batting_stats.Year) AS [Final Year], Min([players_career_batting_stats]![year]-Year([players]![date_of_birth])-IIf(Month([players]![date_of_birth])>=7,1,0)) AS [Debut Age], Max([players_career_batting_stats]![year]-Year([players]![date_of_birth])-IIf(Month([players]![date_of_birth])>=7,1,0)) AS [Final Age], Sum([players_career_batting_stats]![h])/Sum([players_career_batting_stats]![ab]) AS [Avg], (Sum([players_career_batting_stats]![h])+Sum([players_career_batting_stats]![bb])+Sum([players_career_batting_stats]![hp]))/(Sum([players_career_batting_stats]![pa])-Sum([players_career_batting_stats]![sh])) AS OBA, (Sum([players_career_batting_stats]![h])+Sum([players_career_batting_stats]![d])+Sum(2*[players_career_batting_stats]![t])+Sum(3*[players_career_batting_stats]![hr]))/(Sum([players_career_batting_stats]![ab])) AS SLG, (Sum([players_career_batting_stats]![h])+Sum([players_career_batting_stats]![bb])+Sum([players_career_batting_stats]![hp]))/(Sum([players_career_batting_stats]![pa])-Sum([players_career_batting_stats]![sh]))+((Sum([players_career_batting_stats]![h])+Sum([players_career_batting_stats]![d])+Sum(2*[players_career_batting_stats]![t])+Sum(3*[players_career_batting_stats]![hr]))/(Sum([players_career_batting_stats]![ab]))) AS OPS, Sum(players_career_batting_stats.g) AS G, Sum(players_career_batting_stats.gs) AS GS, Sum(players_career_batting_stats.ab) AS AB, Sum(players_career_batting_stats.pa) AS PA, Sum(players_career_batting_stats.h) AS H, Sum(players_career_batting_stats.d) AS 2B, Sum(players_career_batting_stats.t) AS 3B, Sum(players_career_batting_stats.hr) AS HR, Sum(players_career_batting_stats.r) AS R, Sum(players_career_batting_stats.rbi) AS RBI, Sum(players_career_batting_stats.sb) AS SB, Sum(players_career_batting_stats.cs) AS CS, Sum(players_career_batting_stats.k) AS K, Sum(players_career_batting_stats.bb) AS BB
FROM teams INNER JOIN (players INNER JOIN players_career_batting_stats ON players.player_id = players_career_batting_stats.player_id) ON teams.team_id = players_career_batting_stats.team_id
WHERE (((players_career_batting_stats.split_id)=1) AND ((teams.level)=1))
GROUP BY players_career_batting_stats.player_id, players.first_name, players.last_name
HAVING (((players_career_batting_stats.player_id) In (30302,30637,30644,1244,16,34629,32739,13,21709,24 170,35520,33203,33210,33218,33246,37714,34634,3324 0,1296,38840,38841,39957,38838,38853,32785,35728)) )
ORDER BY players_career_batting_stats.player_id;
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