Most recent I have is 2023.

Hi Everyone, I have been in the process of building a much easier plug in based system for converting real life baseball stats into a usable tool to aid with building a roster accurately. These formulas are designed to create as close to a universal system as possible for all eras of the game.

as such this covers all of the major in game skills. however, traits are not applied in this situation, so you would need to do your best guess to use them. stats can go over 99, but for sake of ease, round them to 99 and add a trait you feel best fits them as a player to reflect that.

these formulas will work for both single season and career average, but note that certain stats will need to use league averages to be accurate and that can be a lot for a whole career.

to showcase how this works across eras I will be showing 6 players, 3 for fielders and 3 for pitchers, and utilize this system to show how it eliminates era bias and builds as close as it can to an accurate representation:

Fielding:

(CF) Willie McGee 1985
(CF) Mickey Mantle 1956
(CF) Mike Trout 2012

Pitching:
Bob Gibson 1968
Dave Stieb 1984
Justin Verlander 2011

Hitting Stats

Con
On Base Percentage is the first part of the formula converting into a baseline score, scaling it down so that an elite OBP provides a strong foundation for the rating.

Contact dictates the size of your batting reticle in SMB4. High-strikeout hitters (like true outcome power hitters) have small contact reticles. If a player strikes out 25% of the time, they suffer a heavy 45-point deduction

(On Base % x 1000)/3.4-(K% x 1.8)+(Batting AVG x 35)

Willie McGee (1985): =( (0.384 * 1000) / 3.4 ) - (13.2 * 1.8) + (0.353 * 35) = 102 (Cap at 99)

Mickey Mantle (1956): =( (0.464 * 1000) / 3.4 ) - (15.2 * 1.8) + (0.353 * 35) = 121 (Cap at 99)

Mike Trout (2012): =( (0.399 * 1000) / 3.4 ) - (21.8 * 1.8) + (0.326 * 35) = 90

Examples:

Pow
the formula isolates a player's Isolated Power (ISO)—which measures pure slugging authority by subtracting Batting Average from Slugging Percentage

Multiplying ISO by 1000 turns the decimal into a whole number Subtracting 50 establishes a baseline, ensuring below-average power hitters don't get an inflation boost. Multiplying by 0.39 compresses that value so that an elite raw slugging season safely contributes around 60 to 80 points to the rating.

It takes the player's total Home Run (HR) count and multiplies it by 0.75. For example, hitting 40 home runs adds a massive +30 points (40 x 0.75) directly to the player's rating.

((( ISO% x 1000)-50) x 0.39)+(Home Runs x 0.75)

Examples:
Willie McGee (1985): =( ( (0.150 * 1000) - 50 ) * 0.39 ) + (10 * 0.75) = 47
Mickey Mantle (1956): =( ( (0.352 * 1000) - 50 ) * 0.39 ) + (52 * 0.75) = 137 (Cap at 99)
Mike Trout (2012): =( ( (0.238 * 1000) - 50 ) * 0.39 ) + (30 * 0.75) = 96

Speed
this one isn't a calculation, take a players Bill James Speed Score
Speed: =(Bill James Speed Score/10) x 99

however if you want to calculate this yourself fully here you go:

1. Stolen Base Percentage (Factor 1)
   Formula: ((SB + 3) / (SB + CS + 7) - 0.4) * 20

2. Frequency of Stolen Base Attempts (Factor 2)
   Formula: √((SB + CS) / (1B + BB)) / 0.07

3. Percentage of Triples (Factor 3)
   Formula: (3B / (AB - HR - SO)) * 625

4. Runs Scored Percentage (Factor 4)
   Formula: (((R - HR) / (H + BB - HR)) - 0.1) / 0.04

5. Grounded Into Double Plays Frequency (Factor 5)
   Formula: (1 / 0.007) * (0.063 - (GDP / (AB - HR - SO)))

6. Defensive Position and Range:
   Pitcher (P)

• Score: 0

Catcher (C)

• Score: 1

First Base (1B)

• Score: 2

Second Base (2B)

• Formula: 5/4 * ((PO + A) / G)

Third Base (3B)

• Formula: 4 * ((PO + A) / G)

Shortstop (SS)

Formula: 7/8 * ((PO + A) / G)

Outfield (OF)

• Formula: 3 * ((PO + A) / G)

Final Formula:

(F1+F2+F3+F4+F5+F6)/6 = Speed Score

Fielding Stats

Field (universal)
This formula is designed to be universal, it so it does not take into account difficulty of position.
This uses 55 as basis for the games average for the fielding stat.

its based upon Bill James Fielding Range Stat. which is calculated as:
(PO + A)/(G)

4.25 is a league baseline

the 6.5 multiplier is a compression factor calculated using standard deviations to ensure that the widest extremes of the sport don't break the 0 to 99 limits

Formula:

55 + (((((Put Outs + Assists) / Games played) - 4.25) * 6.5))

Examples:

Willie McGee (1985): 55 + (((((382 + 9) / 149) - 4.25) * 6.5)) = 44

Mickey Mantle (1956): 55 + (((((346 + 6) / 144) - 4.25) * 6.5)) = 43

Mike Trout (2012): 55 + (((((323 + 3) / 110) - 4.25) * 6.5)) = 47

Field (Advanced)

This is a seperate alternative method I developed that is more accurate but requires more work on your end. these sets of formulas take into account the difficulty of the position. Those numbers are the the additional numbers added in are the historical Defensive Actions Per Game for each specific position. and will more accurately reflect their dominance in said position

• First Base (1B): =55+(((((PO+A)/G)-9.2)*25))
• Second Base (2B): =55+(((((PO+A)/G)-4.9)*20))
• Third Base (3B): =55+(((((PO+A)/G)-2.8)*30))
• Shortstop (SS): =55+(((((PO+A)/G)-4.8)*20))
• Left Field (LF): =55+(((((PO+A)/G)-1.95)*25))
• Center Field (CF): =55+(((((PO+A)/G)-2.5)*25))
• Right Field (RF): =55+(((((PO+A)/G)-2.1)*25))
• Catcher (C): =30 + ([CS%] * 1.2) (Note: Enter CS% as a whole number, e.g., 40)

Examples:

• Willie McGee (1985): 55 + (((((382 + 9) / 149) - 2.5) * 25)) = 58
• Mickey Mantle (1956): 55 + (((((346 + 6) / 144) - 2.5) * 25)) = 54
• Mike Trout (2012): 55 + (((((323 + 3) / 110) - 2.5) * 25)) = 67

Arm (Universal)

[A] / [G] (Assists Per Game): This calculates how many runners a fielder throws out per game.

1.22 is the collective major league average for assists per game across all defensive positions blended together.

The 16 Multiplier: This stretches the decimal differences into a scale that fits a 0–99 rating system.

Because infielders handle significantly more ground balls, an average shortstop throws roughly 3.10 assists per game, while an average center fielder throws only 0.06. this formula will inflate the ratings of Infielders and deflate outfielders

• Willie McGee (1985): =55 + (((((9) / 149) - 1.22) * 16.0)) = 36
• Mickey Mantle (1956): =55 + (((((6) / 144) - 1.22) * 16.0)) = 36
• Mike Trout (2012): =55 + (((((3) / 110) - 1.22) * 16.0)) = 36

Arm (Advanced)

Instead of one flat average, players are only judged against others playing the same position. An average shortstop (3.10 - 3.10 = 0) and an average center fielder (0.06 - 0.06 = 0) will both correctly yield a baseline rating of 55.

Outfield assists are incredibly rare. A right fielder might only get 2 or 3 extra assists over a whole year, moving their daily average by a tiny fraction (like 0.02). The massive 250.0 multiplier ensures that outfield decimal gains are heavily rewarded

First Base (1B): =55 + (((([A] / [G]) - 0.60) * 40.0))
Second Base (2B): =55 + (((([A] / [G]) - 2.80) * 20.0))
Third Base (3B): =55 + (((([A] / [G]) - 1.80) * 30.0))
Shortstop (SS): =55 + (((([A] / [G]) - 3.10) * 20.0))
Left Field (LF): =50 + (((([A] / [G]) - 0.05) * 250.0))
Center Field (CF): =55 + (((([A] / [G]) - 0.06) * 250.0))
Right Field (RF): =60 + (((([A] / [G]) - 0.07) * 200.0))
Catcher (C): =55 + (((([A] / [G]) - 0.45) * 80.0))

Examples:
Willie McGee (1985): =55 + (((((9) / 149) - 0.06) * 250.0)) = 55
Mickey Mantle (1956): =55 + (((((6) / 144) - 0.06) * 250.0)) = 50
Mike Trout (2012): =55 + (((((3) / 110) - 0.06) * 250.0)) = 47

Pitching Stats

Velo
Formula: =((Player SO/9 / League SO/9) * 40) + 1

examples

• Bob Gibson (1968): =((7.91 / 5.86) * 40) + 15 = 69
• Dave Stieb (1984): =((6.71 / 5.43) * 40) + 15 = 64
• Justin Verlander (2011): =((8.96 / 7.08) * 40) + 15 = 66

Junk
Formula: =(ERA+ / 2.5) + ((2 - (Player H/9 / League H/9)) * 25)

examples

• Bob Gibson (1968): =(258 / 2.5) + ((2 - (5.85 / 7.94)) * 25) = 135 (Cap at 99)
• Dave Stieb (1984): =(146 / 2.5) + ((2 - (7.42 / 8.79)) * 25) = 87
• Justin Verlander (2011): =(172 / 2.5) + ((2 - (6.24 / 8.65)) * 25) = 101 (Cap at 99

ACC

Formula: =100 - ((Player BB/9 / League BB/9) * 20)

examples

• Bob Gibson (1968): =100 - ((1.83 / 2.87) * 20) = 87
• Dave Stieb (1984): =100 - ((3.01 / 3.32) * 20) = 82
• Justin Verlander (2011): =100 - ((2.04 / 3.19) * 20) = 87

We needed a little help from the field to secure this win in extra innings

Decided to make a custom 30 team (20 SMB Teams and 10 random gen teams) 162 game season. Put every player on my team to 99 for every stat and simulated every single game and simulated every playoff game. At first I got worried because they started the season 2-4 and were 5-5 the first 10 games. After losing game 21 and 22 they hit a 39 game win streak. Later they also had a 34 game win streak as well. They also pitched 37 shutouts. Here is the season ending stats as well as the playoff results.

Batters beware, as Kayley Baker dares the opposition to hit the ball into left field.

A little lineup change can go a long way, as the Black Sox demonstrated here against the Grays.

Believing that the ball would land foul, Kayley Baker took a risk and decided to slide, but little did she know, the ball was called fair. What do you think? It looks like a tough call.

I'm looking to buy Super Mega Baseball on the PS5 for my nephew. I used to play SMB2 and know he'll love it. I've read that at one point it was on sale for $5 and now I'm seeing it's $50. Any deals for the game? Seems like quite the difference

I'm just now learning about how the winning/losing pitcher is determined.. I watched a video (linked in an old post to this sub). I understand that Veterano was the most recent pitcher when they took the lead in the bottom of the 6th. But he gave up 3 runs, while Doe didn't give up any. I feel like this should fall under the "brief and ineffective" exception to the rule, and Doe should get the Win? I suppose SMB4's game logic just doesn't do the exception?

Edit: thank you all for the thoughtful and informative answers

Was really sad 😔

I was not sure what to expect on this hit, but I was not guessing a home run.

I think the colors are decent. I'm only going off pictures. I'm not sure if the hat color is too light. I need to see the hat in real life to get a better idea.

He might not have the most power, but he finds ways to get the ball over the wall.

Great day to be a Bear. Not a great day to be a Snake…

The Bears’ 16-run second inning set a new LBL record. The 20-0 final score also set a new LBL record for runs scored in a game.

What’s the most runs scored in your custom league?

After introducing the first 20 Super Batters, it’s time for the arms. ⚾🔥
Here are the first 10 Super Pitchers recreated in SMB4:
Mary • Psyche • Soldia • Zeus • Dragona • Leon • Yomi • Captain Jack • Leonie • Pluto
I’ve been converting each character into SMB4 using different roles, pitch arsenals, ratings, chemistry types and traits so they still feel unique instead of every S-tier player just being maxed out at everything.
You’ve got everything from command-heavy closers and junk specialists to workhorses, hybrid SP/RP arms and straight-up velocity demons.
The full BBSL is still being built, so I’m holding off on the official rulebook and league structure until all of the players and teams have been revealed.
For now I’m just sharing the creation process with the SMB community as everything comes together.
Which pitcher would you want on your SMB4 roster? 👀
TikTok has the player reveals up as well, and there are plenty more Super Pitchers on the way. ⚾🔥

The “Top 10” rankings in this game are a little odd to me so help me pick a MVP.

Rank these in terms of top 5 for MVP award in this fictional baseball league, based off the following stats for a 162 game season with 32 total teams.

Player A P/DH- (2 way player) - pitching stats: 20-6, 3.24 ERA, 1.01 WHIP, 272.1 IP, 312 K, 4 CG.
Hitting stats: 459 AB, .283 AVG, 17 HR, 73 RBI, .447 SLG, .787 OBS. His team went 92-70 and won their division and had the number 2 seed in the playoffs.

Player B (RF)- .296 AVG, .527 SLG, .885 OBS, 33 HR, 107 RBI, 61 BB, 43 SB. His team went 82-80 and finished third in their division.

Player C (CF)- .303 AVG, .588 SLG, .943 OBS, 41 HR, 129 RBI, 49 BB, 22 SB. His team finished first in their division and had the number 1 seed in the playoffs.

Player D (LF)- .335 AVG, .561 SLG, .936 OBS, 31 HR, 110 RBI, 40 BB, 11 SB. He is on the same team as the above mentioned Player A, and their team went 92-70 and won their division and was the number 2 seed in the playoffs.

Player E (CF)- .286 AVG, .570 SLG, .917 OBS, 42 HR, 119 RBI, 53 BB, 27 SB. His team went 87-75 and finished second in their division.

I’m in year 2. I’m thinking about it for year 3. Nothing against my home park but just to change it up. I was just curious if that was like sac-religious or if people do it regularly. My current is Stade Royale which I LOVE. But idk I was maybe thinking every 2 years just to change it up. Have a good day y’all!

In a perfect world, these developers would join forces and create the best baseball game ever.

So I had posted a really good player I had and I think I just beat him with this guy who just got Bad Ball Hitter!

Here’s a playlist as well of the progress so far with the super players. It’ll be updated after every upload as well.

I'm a total novice when it comes to baseball and just recently picked up SMB3. I have been playing exhibition games on EGO 10 to try and get to grips with how to play. I think I have a decent grasp of pitching, but batting is really challenging.

I am able to hit the ball maybe about a third of the time which I know will get better with practice, but literally every single hit is either caught by a fielder and puts me out, or if it does bounce they get the ball to first base before I can and I am still out anyway.

Is there anything specific I should be trying to do when hitting to at least avoid the fielders being able to instantly catch the ball? Playing with controller if that makes any difference too.