Baseball Mogul: Overall Ratings

As mentioned before, Baseball Mogul used to have the problem that player ratings weren’t well-defined. For example, an 85 Contact rating might put a batter among the top 25 players in the league in this category, while an 85 Power rating might not be enough to break the top 100.

There was also a problem of rating drift. When you start a new game in 2011, the MLB average for Contact is 78; ten years later, it has risen to 82.

These problems were fixed in Baseball Mogul 2016 and further improved in Baseball Mogul 2017. Player ratings are now well-defined in terms of what each number means relative to the league. And ratings are constantly adjusted every to ensure that these definitions remain meaningful over multiple seasons.

However, I’ve gotten some questions about how the ratings are actually defined. So, I pulled a couple tables from my design documents to help clear this up. This one shows how Overall ratings are groups into 10 separate categories, each with its own general definition:

This table shows a typical distribution of players in a 30-team league:

Players in Database:  The number of players that should fall within this range in a modern database with 30 teams.

Roster Spot(s): The roster spots that players of this caliber generally fill on a major league roster. For example, players rated 77-78 will occupy roster spots #16 to #20 on an average team; they aren’t good for the starting lineup or pitching rotation (a total of 13-14 players) but they have value off the bench and out of the bullpen.

Improvements to Baseball Mogul 2017

Baseball Mogul 2017 includes more than 130 improvements and bug fixes. As you would expect, we have included hundreds of new major and minor league players, and all MLB player and team statistics through 2016 (including the playoffs, off-season awards, and updated contracts and salaries).

But Baseball Mogul 2017 also includes proprietary data that you can't find anywhere else:

• Hand-edited pitch repertoires and fastball velocities for more than 6,000 pitchers from 1881 through 2017.
• More than 600,000 lines of PITCHf/x pitch data, including velocity and usage patterns for every season from 2002 and 2016.
• More than 1.9 million lines of minor league stats from 1880 through 2016, at every level from AAA down to the 'D' leagues.

I've also made some major improvements to the simulation engine:

The first is a new model for improvement and decline at different defensive positions. The sim continues to adjust ratings based on age and playing time, but I've added a lot more data to the model. This means that each specific ability (such as an outfielder's range or a catcher's ability to hold runners) is calculated from all available data major and minor league data (including some data from foreign leagues). Additionally, each ability at each position has its own "maturation curve" -- and these curves change over time from 1890 through 2017 according to my analysis of the historical data.

The second is a new system for normalizing historical stats, leading to much more accurate player ratings and simulation results in historical years. The game now calculates individual averages and standard deviation for each statistic by season, defensive position and stadium. (Previous code used all-time averages for standard deviation, and normalized player statistics over their entire career.)

Finally, I planned a longer beta-test cycle this year, ensuring that the initial release was much more stable and bug-free. Baseball Mogul 2017 went on sale three days ago and the most serious bug that's been reported so far is that the home and away teams are flipped in the 2017 MLB schedule (this bug has already been fixed in the version currently for sale in our store).

Anyway, here's a longer change list. I'm sorry I haven't been able to describe these improvements in more detail, but it's easier for me to code and test changes and new features than it is for me to write about them.

2017 Database

• Updated major and minor league statistics from 1880 through 2016.
• Added biographical data for all 2016 MLB debuts and hundreds of new minor league players.
• Updated 40-man rosters for opening day.
• Imported more than 600,000 additional lines of PITCHf/x pitch data.
• Added filters to fix data errors on-the-fly

Historical Simulations (1901 – 2016)

• Rewrote code for deriving player ratings and statistical projections from historical data.
• Improved historical accuracy for fielding error rates.
• Improved historical accuracy for determining pitcher "Endurance" ratings.
• More accurate defensive ratings, especially for designated hitters and pinch hitters.
• Incorporated more data in player aging model.
• Created more accurate career paths in both historical and modern leagues.

Simulation Engine

• New model for player improvement at defensive positions.
• More realistic adjustments for playing time at all levels.
• Improved player health model and injury rates.
• Improved realism for platoon advantage by player type.
• Reworked code for simulating minor league games.
• Updated Win Expectancy data.
• Improved logic for intentional walks.
• New system for generating fictional players to improve long-term stability of talent pool.
• Bug fixes (e.g. small errors in Payroll Budget related to a bug estimated concessions revenue).

Artificial Intelligence

• Improved selection of defensive starters and starting lineup
• More realistic use of defensive substitutes
• Improved logic for 25-man and 40-man rosters
• Improved long-term (multi-year) roster management
• Improved management of pitching rotation
• Includes better logic for picking "alternate starters" from active roster
• Bug fixes (such as the computer over-riding changes to a player’s position)

Interface / Options

• Improved Player Rating Editor
• (e.g. dynamic updating of Predicted stats when ratings are changed)
• Improvements to Sortable Stats Dialog
• "40-Man Rosters" added as selectable category when viewing/sorting.
• "Pitches Thrown", "Strikes Thrown", "Strike Percentage" and "Pitches/Game" added.
• "Steal Tendency" and "Steal Success" player ratings added to Sortable Stats.
• Improved heat maps (in Charts Tab of Scouting Report).
• Improved interface and readability in Play-By-Play screen.
• Optimized League Builder to increase speed by about 400%.
• Schedule importer now supports 3-letter team abbreviations.
• Bug fixes
• Errors converting between rating scales (“50-100”, “20-80”, “1-20” etc.)
• etc.

Baseball Mogul Predicts the 2017 Season

I'm a couple days late, but I put Baseball Mogul 2017 through it's paces today by simulating the current season a thousand times.

The following tables show the average number of wins and losses for each team, plus the chance of each result for each team (such as winning the World Series or finishing last in 3rd place in their division).

AL EAST	Won	Lost	Wins World Series	Wins ALCS	Reaches ALCS	1st	2nd	3rd	4th	5th
Boston	89.88	72.15	102	207	406	763	162	61	11	3
Toronto	85.59	76.49	38	71	148	112	311	320	214	43
Tampa Bay	83.03	79.02	21	44	87	63	304	259	260	114
New York	82.72	79.34	15	31	63	55	212	243	292	158
Baltimore	76.63	85.37	4	7	16	7	11	112	192	678

AL CENTRAL	Won	Lost	Wins World Series	Wins ALCS	Reaches ALCS	1st	2nd	3rd	4th	5th
Cleveland	91.85	70.26	121	246	485	715	203	69	10	3
Detroit	84.19	77.91	30	57	118	232	340	287	131	10
Kansas City	76.66	85.40	3	8	14	55	251	294	293	107
Chicago	74.64	87.41	3	5	12	9	185	254	427	125
Minnesota	69.79	92.22	2	4	9	4	17	79	135	765

AL WEST	Won	Lost	Wins World Series	Wins ALCS	Reaches ALCS	1st	2nd	3rd	4th	5th
Houston	86.36	75.77	81	159	319	422	235	213	116	14
Seattle	85.32	76.75	54	106	214	270	362	261	118	19
Texas	81.94	80.15	15	27	57	213	282	315	156	34
Los Angeles	78.39	83.65	11	19	41	80	146	154	425	185
Oakland	74.34	89.66	8	16	34	26	46	70	247	611

NL EAST	Won	Lost	Wins World Series	Wins NLCS	Reaches NLCS	1st	2nd	3rd	4th	5th
Washington	92.45	68.59	89	187	363	631	307	52	7	3
New York	90.48	71.61	68	138	274	350	542	97	9	4
Miami	80.64	81.37	11	20	44	12	88	401	372	117
Atlanta	74.59	87.43	2	3	7	5	36	389	338	232
Philadelphia	68.27	93.73	1	1	3	2	20	61	279	640

NL CENTRAL	Won	Lost	Wins World Series	Wins NLCS	Reaches NLCS	1st	2nd	3rd	4th	5th
Chicago	94.65	67.38	129	245	505	732	210	51	5	2
St. Louis	84.45	77.60	17	36	70	162	460	229	141	8
Pittsburgh	81.26	80.80	13	25	52	90	252	477	168	14
Milwaukee	71.50	90.50	3	8	17	11	79	234	565	111
Cincinnati	69.66	92.34	1	3	7	5	11	20	127	827

NL WEST	Won	Lost	Wins World Series	Wins NLCS	Reaches NLCS	1st	2nd	3rd	4th	5th
Los Angeles	93.50	68.60	103	210	406	491	258	205	39	17
San Francisco	88.82	73.25	41	86	167	295	363	252	51	29
Arizona	80.13	81.92	8	17	36	103	248	302	292	55
Colorado	78.75	83.27	5	11	21	71	130	235	496	74
San Diego	67.23	92.77	1	3	5	19	32	48	120	781

These tables were created using the new HTML output feature of the Simulation Tool in Baseball Mogul 2017.

Baseball Mogul 2017: Schedule File Formats

Baseball Mogul 2017 now includes support for team abbreviations in schedule files, making it much easier to create or import your own schedule files (for historical and/or fictional seasons).

This is a copy of the document included in the Baseball Mogul "schedules" folder.

Schedule Filenames

The league format is converted to numbers and letters that are used to create the file name. If the schedule is from a specific year, the year is appended to the filename.

This is an example of the name of a schedule file: 162-U-555-555-I-2017.txt.

• ‘162’ is the number of games in the season.
• ‘U’ means the schedule is “unbalanced”. This means that teams play more games within their division, that vs. league opponents. (A ‘B’ would be used to indicate a “balanced” schedule, where all league teams play every other league team roughly the same number of times).
• The first ‘555’ refers to the number of teams in each division of league #1 — the American League has 5 teams in each of 3 divisions. The second ‘555’ refers to the number of teams in each division of the league #2 — the National League also has 5 teams in each division.

Because this schedule is for an unbalanced league, Baseball Mogul needs to ensure that league format has the correct number of teams in each division. Thus, the schedule file includes a number for each division.

For balanced schedules, the game only cares about whether the number of teams in each league match the schedule format. This only requires one number for each league. If the above file were for a balanced schedule, it would use the following filename: 162-B-15-15-I-2017.txt.

• The ‘I’ indicates that the schedule includes interleague games. If the schedule only features league play, this is replaced with an ‘L’.
• By default, all schedules have a 3-day all-star break. If this schedule didn’t have an All-Star Break, '-N' would be appended to the filename (for ‘No all-star game’).
• Finally, the specific year can be added as an optional parameter. If a year is listed, this schedule will only be loaded for that season.

The Baseball Mogul "schedules" folder

Examples

162-B-14-16-I = 162 games, balanced, 14-team AL, 16-team NL, with interleague play.
162-U-554-565-I = 162 games, unbalanced, 5-5-4 AL, 5-6-5 NL, with interleague play.
154-U-77-66-L = 154 games, unbalanced, 7-7 AL, 6-6 NL, no interleague play.
154-U-77-66-L-N = 154 games, unbalanced, 7-7 AL, 6-6 NL, no interleague play, no All-Star Break.

Schedule Formats

All schedule files use one line per scheduled game. Each group of games is preceded by a single
specifying the date on which those games occur.

This date must always be in the format “XXX M/D/Y”. For example, “Wed 4/5/2017” refers to
Wednesday April 5th, 2017. The month and day are the only data used; the year and day of week are
ignored.

Individual games on each day can be represented in one of two formats:

1. Numerical Team Indexes

Teams are represented by numbers, starting at 0, such as the following lines:

Sun 4/3/2017
0 @ 6
1 @ 3

Each number specifies a team in the database, with the teams grouped by division.1 For example, in a
template for the 1977 season:

- Teams 0-6 are the AL East
- Teams 7-13 are the AL West
- Teams 14-19 are the NL East
- Teams 20-25 are the NL West

Teams are ranked alphabetically within each division. Using 1977 again as an example:

- Team 0 is the Baltimore Orioles
- Team 1 is the Boston Red Sox
- Team 2 is the Cleveland Indians
- etc.

Note: Baseball Mogul assumes that leagues are sorted alphabetically, and divisions are sorted from East to West. The “American League” will be listed before the “National League”; the “AL East” will be listed before the “AL Central” and the “AL West”; etc.

2. Team Abbreviations

New for Baseball Mogul 2017, schedule files now support 3-letter abbreviations. For example:

Sun 4/3/2017
SFG @ ARI
NYY @ TBR

Team abbreviations can be viewed (or edited) in the League Editor inside Baseball Mogul.

Schedule Files as Templates

If the specific year is omitted, Baseball Mogul will use the schedule file as a template. Instead of
scheduling the games exactly as specified in the file, it will load the schedule file and then randomize
team schedules within each division. This allows the game to create unique schedules for future years, or for historical years for which we do not yet have specific data.

Schedules are shuffled WITHIN each division. So, in the 1977 example above, if Team 0 is scheduled to play the first game of the season, then the schedule created from that template will feature ANY ONE of the first seven teams to play the first game of the season.

Double-Headers

Baseball Mogul doesn’t currently support teams with more than one game schedules on the same day.
If you are creating a schedule file for a historical year with scheduled double-headers, you will need to move the 2nd game to a day where those teams have a day off (such as by moving the 2nd game of a Sunday double-header to the following Monday).

Missing Schedule Files

If there is no matching schedule file, Baseball Mogul fabricates a schedule using an algorithm than can produce some unrealistic schedules (with large gaps between games played). I am trying to expand the number of schedule files available inside Baseball Mogul. If you have created a file for a missing year or format, please forward it to cjd@sportsmogul.com and/or post it to our mod forums.

Thank you!

Appendix A: Numbers Used in Schedule Files for 2014-2016 Seasons

Note that teams are ordered:

1. Alphaterically by League (AL then NL).
2. Then by division from east to west.
3. Then alphabetically within each division.

AL East
BAL 0
BOS 1
NYA 2
TBR 3
TOR 4

AL Central
CHA 5
CLE 6
DET 7
KCR 8
MIN 9

AL West
HOU 10
LAA 11
OAK 12
SEA 13
TEX 14

NL East
ATL 15
MIA 16
NYN 17
PHI 18
WAS 19

NL Central
CHN 20
CIN 21
MIL 22
PIT 23
STL 24

NL West
ARZ 25
COL 26
LAD 27
SDP 28
SFG 29

Game Company Redesigns Chess

Last week, "Hasbro Gaming" redesigned Monopoly in order to get some free publicity. They added a T-Rex, a penguin, and a rubber ducky.

This caused us at Sports Mogul to think: "Hey, we
could use some free publicityare good at redesigning games!"

So, as a public service to the world, we've decided to bring the game of chess into the 21st century. With the help of the internet.

Step 1. We asked the internet to name their least favorite chess piece. The results:

Rank	Least Favorite Piece	Votes	Percent
11	The Knight	652,510	3.16%
10	The "Horsie"	998,602	4.50%
9	The Rook	1,257,993	6.65%
8	The Unicorn	1,400,590	7.98%
7	The Pawn	2,042,532	9.66%
6	Ringo	2,272,338	10.91%
5	Charmander	2,654,760	12.10%
4	The Queen	2,996,974	14.10%
3	The King	3,110,278	17.09%
2	Fluttershy	4,110,707	22.56%
1	The Bishop	4,169,452	22.70%

Adding Stadium Art to Baseball Mogul

I got an email this morning with the following question:

I am trying to use this photo of Jarry Park in Baseball Mogul, but all the numbers are skewed. The batter is nowhere near home plate, the first baseman is almost on the pitchers mound... what can I do to fix this?

They attached a file called PBP-Stadium-Montreal.jpg:

To verify the problem, I copied this file to my 'Stadiums' folder and started a new game as the 1974 Expos. When I went into Play-By-Play mode for their first home game, the field looked like this:

As reported, the ratings for all the fielders are in the wrong place on the field (as are the outfield fence distances). This is because every stadium photo requires a matching text file that specifies the coordinates that Baseball Mogul needs to use when drawing these labels.

To get these coordinates, open the photo in a graphical editing program. I use the "Paint" program included with Windows. Baseball Mogul assumes a photo size of 640 x 480 pixels, so you should resize your photo before proceeding.

Microsoft Paint displays the coordinates of your cursor in the lower left corner of the window. Start by placing your cursor over home plate and record the coordinates to a text file. Continue this process for each of the following locations on the field, typing each set of coordinates on it's own line. (If the foul poles are outside the picture, estimate their coordinates.)

Home Plate
First Base
Second Base
Third Base
Left Field Corner
Center Field Wall
Right Field Corner

After each pair of coordinates, you can add a comment by typing '//' and the following it with a useful label. When done with PBP-Stadium-Montreal.jpg, my text file looks like this:

318,351 // HOME_PLATE_COORDINATES Bottom corner
515,290 // FIRST_BASE_COORDINATES Outside corner
321,250 // SECOND_BASE_COORDINATES Top corner
128,285 // THIRD_BASE_COORDINATES Outside corner
-47,221 // LEFT_FIELD_COORDINATES Base of foul pole
324,194 // CENTER_FIELD_COORDINATES Base of CF wall
700,229 // RIGHT_FIELD_COORDINATES Base of foul pole

Save this file with the same filename as the stadium photo, but add the ".ini" file extension (e.g. PBP-Stadium-Montreal.ini).

When I go back into Baseball Mogul 2016 and resumed the game with the 1974 Expos, the stadium view now looks like this:

Much better!

"Baseball Mogul is Horse Shit"

We got the following tweet a couple months ago:

... and then this (by someone else) ...

... and this (by yet another person):

I should be pissed off, but these made me smile. I love that people get emotionally invested into their Mogul teams. And it's nice to see people so angry about something other than politics.

These tweets also illustrate what people mean when they say that a game "cheats" or is "rigged". It's not just that the game is difficult — it's that the sim engine has "catch up" code — it waits until you get ahead (in a game or in a season) and then turns on the afterburners to kick your ass.

I briefly worked on the PC version of Madden in the 1990s, and know that it had strong catch-up code (and probably still does). On MLB Slugfest, we created different difficulty levels, but the game didn't cheat — the results were the same regardless of whether you were winning big or losing badly.

I assure you that Baseball Mogul isn't "rigged". There isn't a single line of code in the game that looks at the score (or the standings) and then adjusts the results accordingly. If your team goes 16-8 in April and then 9-18 in May, I guarantee that it's just bad luck. The cheating described in these tweets is purely in the minds of the players. And Baseball Mogul has never contained any catch-up code, so there isn't anything hiding deep in the simulation code that I somehow forgot to remove.

Although the simulation doesn't cheat, the diffculty level does affect the computer GMs' desire to do a deal with you. For example, GMs on "Fan" level will actually make trades with you that they would normally turn down (if offered by another computer-controlled team). Difficulty level also affects the revenue earned by any human-controlled teams.

Difficulty Level	Artificial Intelligence (Computer GMs)	Revenue Adjustment (Human-Controlled Teams)
Fan	Biased in favor of trades offered by human-controlled teams.	+5%
Coach	No adjustment.	None.
Manager	No adjustment.	-5%
Mogul	Biased against trades offered by human-controlled teams.	-10%

As you can see, Baseball Mogul cheats in your favor on "Fan" level, and cheats against you on "Mogul" level. But this adjustment only affect your revenue stream — it doesn't pump up the losing team in order to keep things interesting, and it doesn't penalize your team just because you are on pace to win 100 games.

Football Mogul 17: Historical Seasons, Part 2

Here's some more info on the upgraded historical database in Football Mogul 17:
Lets go back to 1996 again -- a year when Brett Favre would win the MVP and lead his Green Bay Packers to a 13-3 record and 35-21 win over the Patriots in Super Bowl XXXI.

This is Green Bay's offense at the beginning of the 1996 season -- as viewed in last year's version of Football Mogul:

Here are a few problems I see with this screen:

1. All the ratings are too low. Future Hall-Of-Famer Brett Favre only has a 77 Overall rating.

2. Dorsey Levens is listed as the starting fullback, even though Levens was never a fullback.

3. Mark Chmura is riding the bench behind Keith Jackson (Chmura started 13 games for the Packers in 1996).

4. Starting tackles Earl Dotson and John Michels are shown as second-stringers.

And here's the 1996 Green Bay offense in Football Mogul 2017:

As you can see. the starting lineup is correct and the player ratings are more accurate.

Football Mogul 17 Preview: Historical Seasons

Football Mogul 17 has been delayed due to some personal issues in my life. However, we are doing final testing and expect to launch the game on Tuesday (November 8th).

This year's biggest upgrade is an entirely new historical database including over 250,000 lines of season data. Previous versions of Football Mogul included historical teams, but our database had a lot of errors and missing stats so we went back to the drawing board and assembled an entirely new database.

We were able to incorporate some stats, such as snap counts and Yards After Catch, that you can't find at sites like Pro-Football-Reference.com and NFL.com. But the most important result of this upgrade is that the historical team and player ratings are much more accurate.

This is a screen shot from last year's version of Football Mogul, showing the AFC at the beginning of the 1996 season:

If you remember 1996, you may notice that the ratings are way off. The Patriots beat the Jaguars in the 1996 AFC Championship game, but the Jaguars are ranked 21st and the Patriots are ranked 24th (out of 31 teams in the league). 1996 was Drew Bledsoe's best season, but New England's "Quarterbacks" have a grade of "C-".

These problems have been fixed for Football Mogul 17. In addition to having more data, we've also rewritten the way that player ratings are calculated, correcting every line of a player's stats for that year's league averages and that team's strength of schedule.

This results in much more accurate ratings for individual players and across entire teams. The Patroits are now the 2nd best team in the AFC (behind Denver who went 13-3 that year and had a better point differential). The "Quarterbacks" column now has 4 teams with an A-minus or better: New England (Bledsoe), Miami (Marino), Denver (Elway) and Jacksonville (Brunell), Marino and Elway are now in the Hall Of Fame, and Brunell was at his peak in 1996, leading the NFL in passing yards and yards per attempt.

Thank You

My mother passed away a few weeks ago at Middlesex Hospital in Middletown, CT (due to complications from colon cancer). Thanks to a cot provided by the hospital, I was able to spend the last month by her bedside. Although the staff and volunteers at Middlesex are incredibly skilled and attentive, my presence was invaluable at times when she woke up confused or in pain. I was very lucky to have this time with her, and we talked about some parts of her life that she had never shared with me (such as the railroad tracks she played on as a toddler, and the brief affair she had with a married man before joining the Air Force in 1962).

The nurses and doctors kept asking me what my job was that allowed me to "take so much time off". The answer is that I work for Sports Mogul. And while it might cost the company some revenue when I postpone a new version of Football Mogul, it won't cost me my job. For this I am extremely thankful.

So, to everyone who has supported Sports Mogul over the years, thank you -- not just for your direct support that pays our bills, but also for the joy we get every time we hear how much you enjoy our games, and the time and effort that you invest in helping us track down bugs and improve the simulation. Thank you for all of it.

UPDATE: Baseball Mogul Player Ratings

Back in April, I announced changes to the Baseball Mogul rating system for Baseball Mogul 2016.

To summarize:

1) Player ratings had crept up over the years (to the point that the average major league pitcher had an Overall rating of about 83).

2) Furthermore, this rating creep varied by rating. For example, a pitcher in Baseball Mogul Diamond with an 83 Control was in the top 40% of major leaguers. But a pitcher with an 84 Power was actually below average.

3) Because of these problems, Baseball Mogul 2016 featured a new system that defined 75 as the average across all ratings, and standardized the way in which ratings were distributed (the degree to which they clumped around the average value).

(This change had other benefits, such as more accurate historical simulations, and more realistic results in league that combined players from different historical eras.)

4) However, this meant that virtually all player ratings went down by 5-7 points in Baseball Mogul 2016, whether you were starting a game from scratch or resuming a game from a previous version. This came as a shock to many of you, eliciting some complaints that the player ratings were "broken".

Because of item #4, the most recent official patch (Version 19.17) included changes to the Player Rating Scale options (available in Options, on the Tools Menu). These are the four options that are now available:

Rating Scale	MLB Average	Notes
50-100	80	Current default for Baseball Mogul 2016 (version 19.16 and later). The default rating scale for Baseball Mogul Diamond.
25-100	75	Default rating scale for Baseball Mogul 2016 (before Version 19.16).
20-80	50	Scale used by professional scouts (described on FanGraphs).
1-20	13	20-point scale provided for fans of Football Manager.

This means that if you started a game in Baseball Mogul 2016 before version 19.16, you will see a big jump (about 5 points across the board) when you resume this game with the current patch. If you would like to go back to the system with an average rating of 75, you can change this setting in the Options Dialog:

I'm sorry for the above confusion. I personally prefer the rating system centered around 75 (a "C" on the traditional academic scale). But I didn't foresee how annoying this would be to people who had become accustomed to having a team full of players with ratings in the 80s and 90s.

Football Mogul Predicts The 2016 NFL Season

It's that time of year again. We used the beta version of Football Mogul 17 to simulate the upcoming season 1,000 times. Projected standings are shown for each league, including average number of wins and losses for each team during the simulation. "Super Bowl Champs" shows that team's chance of winning the Super Bowl.

The Only Rule Is It Has To Work

I just finished reading my favorite book of the year, The Only Rule Is It Has to Work: Our Wild Experiment Building a New Kind of Baseball Team, by Ben Lindbergh and Sam Miller. As it is about baseball -- and it helped refine the way that I think about the game and how to model it -- I figured I would share my thoughts here.

This book may not be for everyone. But as someone who writes baseball simulations for a living, I have to say that this book is perfect.

The authors start with the sequence of events that landed them with the Sonoma Stompers for the Summer of 2015. One key reason is something that hadn't occurred to me: the general manager of a low-budget team spends most of his time selling tickets and keeping the concessions flowing, so he is happy to get free help building the roster (the task we associate most often with a team's front office).

Then Ben and Sam dig into the nitty-gritty of building a team. They do a great job of laying out all the numbers that they had in front of them for such tasks as: choosing which players to sign; making lineup recommendations; employing extreme defensive shifts; and building detailed reports on opposing pitchers for use by the team's hitters. Seeing the raw data made the book much more enjoyable than if they had just jumped ahead to the conclusions that they reached.

The authors also do a great job of conveying the storylines and emotions associated with the team. It's reminiscient of a movie like Bull Durham: the overarching plot is about baseball players trying to get a crack at the majors, but the most interesting and amusing and important events revolve around the players' individual growth and interpersonal relationships.

Finally, I found this book inspiring as a personal story of humility and frustration, combined with some great insight into how to "make friends and influence people". I would honestly recommend this book to aspiring business leaders or consultants. Ben and Sam are two extremely bright guys with great communication skills. So one could assume that they had an easy time showing up at the Sonoma Stompers and turning the team around. But I know that my life is never that easy -- and I have to admit it's nice to see that their lives aren't either. Although Ben and Sam are nominally in charge of the roster, it's difficult for two guys that never played professional baseball to earn credibility in the clubhouse. But if you keep reading you see that more often than not they are able to succeed at what they try to do -- relying on respect and candor and true generosity, more than on any spreadsheet.

2016 Mock MLB Draft

Jason Groome

A.J. Puk

As many of you already know, this year’s version of Baseball Mogul includes most of the 100 top prospects that are eligible for this year’s amateur draft. As the 2016 database already includes the top 75-80 players for each franchise, we also have a decent understanding of each team’s specific organizational needs.

So I booted up Baseball Mogul 2016 to see how it would handle this year's draft. One advantage of having a full simulation at our disposal is that I was able to simulate the draft 10,000 times and record all the results. The following table shows the most likely result of the first round (although this exact order only occurred in 131 of the 10,000 simulations). The average draft position for each player is shown below as “ADP”.

Pick	Team	Player	Pos	Age	Bats	Throws	Hgt	Wgt	Scouting	ADP
1	Phillies	Jason Groome	LHP	17	L	L	6'6"	220	40/75	1.61
2	Reds	A.J. Puk	LHP	21	L	L	6'7"	230	45/70	1.78
3	Braves	Nick Senzel	3B	20	R	R	6'1"	205	40/70	3.31
4	Rockies	Mickey Moniak	LF	18	L	R	6'2"	190	40/70	4.36
5	Brewers	Corey Ray	CF	21	L	L	5'11"	185	40/75	5.46
6	Athletics	Delvin Perez	SS	17	R	R	6'3"	165	45/75	6.26
7	Marlins	Riley Pint	RHP	18	R	R	6'4"	195	40/75	8.31
8	Padres	Kyle Lewis	RF	20	R	R	6'4"	195	35/70	10.86
9	Tigers	Matt Manning	RHP	18	R	R	6'6"	195	35/70	10.41
10	White Sox	Dakota Hudson	RHP	21	R	R	6'5"	205	40/65	11.46
11	Mariners	Cal Quantrill	RHP	20	L	R	6'3"	185	40/70	11.01
12	Red Sox	Braxton Garrett	LHP	18	R	L	6'3"	190	40/65	12.06
13	Rays	Blake Rutherford	LF	19	L	R	6'2"	190	45/70	10.86
14	Indians	Zack Collins	C	21	L	R	6'3"	220	40/65	14.16
15	Twins	Forrest Whitley	RHP	18	R	R	6'6"	250	40/70	15.21
16	Angels	Bryan Reynolds	CF	21	S	R	6'2"	210	35/70	16.26
17	Astros	Alex Kirilloff	CF	18	L	L	6'2"	195	40/65	16.06
18	Yankees	Kevin Gowdy	SP	18	R	R	6'4"	170	35/70	18.61
19	Mets	Will Craig	3B	18	R	R	6'3"	225	45/65	17.16
20	Dodgers	Jordan Sheffield	RHP	21	R	R	6'0"	185	40/70	18.71
21	Blue Jays	Buddy Reed	CF	21	S	R	6'4"	185	35/70	20.51
22	Pirates	Ian Anderson	RHP	18	R	R	6'3"	170	40/65	20.56
23	Cardinals	Cody Sedlock	RHP	20	R	R	6'4"	210	45/65	19.86

Scouting shows Baseball Mogul’s “Overall” and “Peak” ratings for each player (on the standard 20-80 scouting scale).

... and this table shows how frequently each player was taken with one of the top 3 picks (rounded to the nearest 0.1%):

Player	Pos	Age	Pick #1	Pick #2	Pick #3
Jason Groome	LHP	17	50.1%	39.1%	10.6%
A.J. Puk	LHP	21	39.2%	45.5%	14.4%
Nick Senzel	3B	20	6.8%	10.2%	42.1%
Mickey Moniak	LF	18	1.4%	2.6%	11.3%
Corey Ray	CF	21	1.0%	0.9%	9.0%
Delvin Perez	SS	17	0.7%	0.5%	6.2%
Riley Pint	RHP	18	0.5%	0.8%	3.5%
Kyle Lewis	RF	20	0.2%		0.7%
Matt Manning	RHP	18		0.3%	0.4%
Dakota Hudson	RHP	21			0.3%
Cal Quantrill	RHP	20	0.1%	0.1%	0.4%
Braxton Garrett	LHP	18			0.3%
Blake Rutherford	LF	19			0.2%

As you can see, Baseball Mogul was ambivalent about whether to take Puk or Groome with the first pick. Twelve years after Moneyball publicized the argument that college players had been perennially under-valued, front offices still frequently use top picks on high school players — so Mogul's drafting AI models that behavior.