Tagged: Sabermetrics

2016 MORPS Projections

2016 MORPS projections are finally ready.  Unlike the baseline projections published several weeks ago, these projections include all players projected to win a MLB roster spot on opening day.  I have also included a number of impact rookies who are projected to join a roster early or mid-season in 2016.  Rookie projections use stats generated during either minor league or international play.  Factors are applied to adjust the stats to MLB equivalent stats.  MORPS projections also account for expected adjustments in personal playing time.

The excel version of the projections include a key tab that defines all headings used in the projections.  In short, fantasy baseball players that play in rotisserie leagues should key on the R-ROTO and ROTO columns.  ROTO is a point value derived from weights on the categories in a standard 5×5 rotisserie league.  R-ROTO is the player ranking based upon the ROTO point values.  If you league uses a customer scoring system, you can use the projections in the categories of interest to customize your rankings.  Fantasy baseball players that play in a more realistic format like Baseball Manager or a similar simulation league should reorder the pitching based upon OERV and the batting based upon RC.  OERV stands for Out Earned Run Value.  This stat attempts to value a pitcher by combining ERA with the value of number of innings pitched.  This is a way for fantasy managers in simulation leagues to compare the value of a relief pitcher with a starter or a starter who pitches 200 innings with one that pitches 100 with a slightly lower ERA.  RC is Runs Created.  A league like Baseball Manager uses RC as a basis for the points they generate in their daily games.  The more realistic the simulation, the closer the hitting will align with RC.

2016 MORPS Batting Projections (XLS)

2016 MORPS Batting Projections (PDF)

2016 MORPS Pitching Projections (XLS)

2016 MORPS Pitching Projections (PDF)

For those who like to resort the projections for their own fantasy system, make sure you filter out the players with a roster status of “N”.  These players will most likely not make an opening day 25 man roster.  Those players who were still in competition for a position were included with a roster status of “Y” in most cases.  I posted the “N” players for those managers who have keeper leagues or deeper rosters that may want to pull one of these folks onto their list.

Team projections for 2016 will be posted within the next week.

Play Ball!

2014 Updates for MORPS Projections

Spring Training always adds unexpected twists for projection systems.  This year is no different.  Injuries, position battle updates, and unexpected player transactions lead to changes in player projections.  During the year, this simply leads to variance from projected player performance.  During Spring Training, projection systems have a chance to make last-minute corrections to account for all these changes.

MORPS updates for pitchers and batters are as follows:

2014 MORPS Batting Projections 20140313 (XLS)

2014 MORPS Batting Projections 20140313 (PDF)

2014 MORPS Pitching Projections 20140313 (XLS)

2014 MORPS Pitching Projections 20140313 (PDF)

Feel free to leave comments or suggestions.

Obie

2014 MORPS Projections

MORPS projections are late coming this year.  I’ve delivered a set of baseline projections several weeks ago.  However, you’ll find that the actual projections have some drastic differences.  I’m always amazed by the amount of player movement during the off season.

2014 MORPS Batting Projections 20140227 (XLS)

2014 MORPS Batting Projections 20140227 (PDF)

2014 MORPS Pitching Projections 20140227 (XLS)

2014 MORPS Pitching Projections 20140227 (PDF)

Batting Projections

The Major-League Obie Role-Based Projection System (MORPS) uses four years of player performance data for all hitters. Since I started playing with Sabermetrics using Tango’s Marcel system, the first iteration of MORPS four years ago used the same formulas. After learning the basics, the batter formulas were adjusted to include the most recent four years of performance data. Adjustments were also made for player age, home ballpark data and expected playing time. The most complicated part of the system is the regression formulas. Tango provided formulas for his three year model. I had to crack open the math books to figure out how to transition the formulas to a four year model.

One of the most time consuming tasks in developing the system was determining the proper mean for player regression. If the goal was to ensure that the mean of all the projections competed favorably with end of year player means, the task would have been straight forward. However, my goal was to make the actual player projections as accurate as possible. “Role-Based” means that the player projections are regressed to position specific means. National League means are also separated from American League means.

While conducting research, I noticed that most projection systems used minor league stats as well as any available major league stats to project the future performance of young players. There are even formulas that anticipate player regression when entering the majors. The interesting thing is that Tango’s Marcel system does just as good at predicting rookie performance as other projection systems and he doesn’t use any minor league stats. Some players are great in the minors and simply can’t make the jump to the major leagues. Some players start out great, but find that major league pitchers start exploiting weaknesses they never knew they had. Others outperform all expectations. By calculating the reliability of a player’s projection using only major league data, MORPS adds a proportional dosage of a player’s positional mean to complete a rookie’s player projection. Since we are focused on individual player performance, I didn’t see the point of including all minor league stats when the results don’t seem to provide significant value. The last year of a rookies minor league or international season is included, with appropriate adjustments for competition, if no major league experience exists.  While efforts have been made to adjust projections to reflect anticipated playing time, players who have a roster flag of “N” are projected using baseline projections only.

Pitching Projections

The formulas used to create pitcher projections are very similar to those that we have already discussed with batters. MORPS uses four years of data to create a pitcher projection. Adjustments are made for age, home field and anticipated role. The reliability of a projection is calculated based upon the amount of data available for a particular player. Someone with low reliability will regress more to a position specific mean than someone that has faced a lot of major league batters over the last four years.

The big difference between projecting pitchers and batters is the usage disparity between relief pitchers and starting pitchers. A good relief pitcher may face 350 batters in a season. A top end starting pitcher may pitch to 900 batters in a season. The plate appearances for position players are typically not dependent on role. A first baseman and shortstop may both have 600 plate appearances over the course of a year. Their position means will be different. First basement will typically have higher power stats while shortstops have higher speed stats. But, they are similar enough that their projections can be calculated using the same basic formulas. The disparity between relief and starting pitchers forces them to be calculated very differently. For months I struggled with pitching projections. When I finally figured out that starting pitchers and relief pitchers had to be calculated separately, everything fell in place.

2014 MORPS Baseline Projections

I’ve received several emails asking about 2014 MORPS projections.  My day job now includes travel which has left me less time to work on these projections.  In the interest of time, I have put together a quick and dirty baseline version of 2014 MORPS projections.  “What does this mean?” you may ask.  Well… the short story is that the projections do not include any player team changes or role changes.  I also did not error check.  Will Cano’s stats go down in Seattle?  Absolutely, but this set of projections have not accounted for his change in venue.  You will need to take this into account if you are preparing for an early draft.  Those things being said, the projection engine is the same one I automated last year.  This means that the projections are still based on four years of data, positional mean regression, etc.  In most cases, the numbers are fairly close to final values.  Time permitting, I hope to publish a set of updated projections during Spring Training that include player roles and team changes.

Baseline 2014 MORPS Batting and Pitching projections are available in excel and PDF formats.  Follow the links below to download your copy.

2014 MORPS Batting Projections Baseline (XLS)

2014 MORPS Batting Projections Baseline (PDF)

2014 MORPS Pitching Projections Baseline (XLS)

2014 MORPS Pitching Projections Baseline (PDF)

If you player Roto baseball, you will find the projections already sorted in Roto Rank order.  If you play a more realistic version of fantasy baseball, like BBM, you will need to resort the XLS spreadsheet in RC order for batters and OERV order for pitchers.

Play Ball!

Update of 2013 MORPS Projections

Updates have  been posted for both batting and pitching projections.  These updates include all players that are currently projected to make each team’s 25 man roster according to MLBDEPTHCHARTS.  A large number of non-roster players have also been included.  However, non-roster players have not been “modeled” for MORPS projections.  This means that their projection is based only on historical and mean data.  All active players are assigned a rotorank prior to non-roster players.  Thus, all non-roster players are at the end of the MORPS projections.  This includes free agents.  If some of these players actually win a roster position, compare their roto column to those of active players to decide where they should be slotted.  For simulation leagues, you would use the RC column for batters and OERV for pitchers.

2013 MORPS Batting Projections

2013 MORPS Pitching Projections

As players are signed and spring training position battles are settled, I will plan on updating the projections.  This will occur periodically until the season starts.

What is OERV?

You will notice a new stat category has been introduced that is unique to MORPS – OERV. OERV stands for out earned run value. This new stat attempts to rank pitchers based upon a combination of performance (earned runs allowed) and the number of outs generated for their team. For example, Aroldis Chapman is expected to have a slightly better ERA than Matt Cain. However, Cain is projected to pitch 211 innings compared to Chapman’s 175. As a result, Cain’s OERV is better than Chapman. For those that play rotisserie baseball, a combination stat like this may not have value. You just want to get the best players in each of X specific categories. Head to head simulation leagues, like baseball manager (BBM), use sabermetric calculations to determine daily winners. These leagues will probably find this new stat very useful. This stat attempts to answer the old question that every fantasy manager in these leagues ask on draft day – “When should I opt for a pitcher that eats innings over the pitcher with a lower ERA”.

Predicting MLB Team Performance

Every year you see tons of websites predicting which MLB teams have made the right moves to get their team to the playoffs. Some make their predictions based upon their inane baseball IQ. Others use a popularity approach, which teams are getting the most press or the teams that have landed the big name free agents. Perhaps some sites use dart boards or drawing names from a hat. How else can you explain sites that predict the Cubs or Astros getting to the playoffs! Well, we are going to take a little different approach.

Bill James, the pioneer of baseball sabermetrics, created a formula to predict a team’s winning percentage called “The Pythagorean Expectation”. Without boring everyone with the fine details, this formula models the winning percentage of a team based upon runs scored and runs allowed. With anticipated starting and reserve lineups, MORPS has already projected runs created and runs allowed for each team in order to create individual projections. By feeding this data into a refined version of Bill James’ formula created by David Smyth, MORPS team win/loss records can be projected. The records are then adjusted slightly to show the number of games played within each division, league and inter-league matchups.  This adds an element of anticipated strength of schedule to a set of formulas created to model the past rather than predict the future.

In the next few days/weeks, I will plan on releasing projected wins and losses for the teams in MLB. I was hoping that all the big name free agents would be off the board at this point, but progress can’t wait on hard-headed agents or budget conscious General Managers. Since my main goal leading up to spring training is to continue updating MORPS for upcoming fantasy drafts, I will begin releasing team projections very soon. When final free agents sign contracts, projected wins and losses may adjust slightly. I may go back and update team projections prior to the start of the season if time permits.

For those that are interested in the detail, I have outlined several the formulas below that are used in team win/loss projections.

The Pythagorean Expectation (developed by Bill James)

Pythagenpat formula (developed by David Smyth)

Exponent = ((r + ra)/g)0.287

Runs Created (developed by Bill James) – calculated for each individual player

2013 MORPS Pitching Projections

The pre-season version of the 2013 MORPS Pitching projections are also now available in excel and pdf formats.  Follow the links below to download your copy.

2013 MORPS Pitching Projections 20130323 (XLS)

2013 MORPS Pitching Projections 20130323 (PDF)

The pitching projections took a little more time to get ready.  I’ve added some logic this year that adjusts the historical numbers when a player moves from a relief to a starting role or vice versa.  A good relief pitcher and good starting pitcher can have 500 to 600 difference in batters faced over the course of a season.  This makes it impossible to directly translate a relief pitchers performance to a starting role or a starter into a relief role.  I’m hoping that my new logic solved this puzzle, at least from the point of view of sabermetric projections.

Projection Team-by-Team Modeling

I’ve completed the projection modeling process of 26 teams at this point. Only a few more left. As I was reflecting on this tedious modeling process, I though some might be interested in how it is done.

I’ve already shared how batter and pitcher projections are created. The role based portion of MORPS is the regression to the mean based upon the primary position of a player. Player roles are further refined during the team modeling process by evaluating each player that has been projected to make the 25 man team roster. Free agent acquisitions, retirements, changes in team strategy and a variety of other things will impact the positions of a player during the upcoming season. The art of MORPS projections is analyzing all these movements, team-by-team, and shifting position regression and anticipated plate appearances or batters faced. If I was simply concerned about how MORPS stacks up against other projection systems, I wouldn’t worry about these tweaks. MARCEL projections have shown us that simple projections will be very close to actual when taken as a whole without these adjustments. However, this doesn’t help a fantasy owner who wants to know if they should select player A on draft day. The team-by-team analysis allows MORPS projections to reflect the anticipated roles of a player for the upcoming season. This is also why the projections are updated several times leading up to the season. Roles change based upon Spring Training decisions. Those change then impact individual player projections.

So… How does team-by-team modeling work? The first step is to calculate the total number of plate appearances and batters faced for each team position, batting order position, etc. Although one can argue that a team will never have the same number of plate appearances or batters faced from year to year, we are assuming that the variation in these numbers from year to year will not be dramatic. I then take projected lineups, including backups, and assign them to the position or positions they are anticipated to play. Plate appearances and batters faced are adjusted to reflect anticipated playing time and primary positions are adjusted for those players that are moving to a new role in the upcoming year. Projections are then recalculated for each player on that team using these adjustments. Unlike last year, I’ve written a windows program that simplifies this process. That being said, it is still tedious and time consuming.

When all the team projections are completed, I will then put all the players together into batting and pitching lists in preparation for publication. The last check is to scan position totals for abnormal variances that would indicated mistakes in the adjustments. Those things are then fixed and we’re ready for publication. With only a handful of teams left, I am hoping to have the pre spring training projections out within the next several days.

Let me know if anyone has questions. I figure the more open I am with the process, the more people will be able to trust the end result.