# Tagged: OPS

# 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 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.

# 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.

# OPS, wOBA and the BBM Total Offense Formula

First, let’s define our terms. OPS stands for on-base plus slugging. It is the combination of a player’s on-base percentage (OBP) and their slugging percentage (SLG). For many fantasy owners, it is the most convenient way measure players against each other since it values many the variables used in fantasy resolution formulas. wOBA stands for weighted on-base average. This is a statistic that was created by Tom Tango and presented in “The Book: Playing the Percentages in Baseball”. Tom makes the case that OPS is not an accurate reflection of the true run production of a player. By analyzing “linear weights” or the contribution that a single, double, etc. makes toward a run being produced, he devised a formula that calculates wOBA. This new statistic is has a similar scale to the more traditional OBP (see article). Lastly, the BBM Total Offense formula is the fantasy resolution formula used by Baseball Manager (BBM) to calculate offensive runs produced during daily head-to-head games (see BBM guide).

In order to figure out which stat is the best at projecting runs, I calculate each stat for the top 300 players in 2011. I then converted the rates stats (OPS and wOBA) into counting stats by multiplying them by total plate appearances. I ran the numbers through a correlation function. The results are shown below.

Correlation |
OPSc |
wOBAc |
BBM |

Runs (R) |
0.919259 |
0.929574 |
0.900852 |

As you can see, the correlations are all over 90%. However, the converted numbers for weighted on based average seems to have the best correlation to actual runs scored by the top 300 players in 2011. It is important to note that none of these formulas include runs as a component of the formula. Thus, the variables are all completely independent.

For completeness, I ran the same correlation as above against my projection data from last year and actual runs produced. The results are outlined below.

Correlation |
xOPSc |
xwOBAc |
xBBM |

Runs (R) |
0.497973 |
0.494041 |
0.527327 |

I was surprised that the BBM total offense formula had a better correlation than either projected wOBA or projected OPS. I had expected the correlations shown with 2011 actuals to extend to projected numbers. My immediate concern was that the results were caused by some anomaly within my 2011 projections so I decided to run the same calculations using published Marcel projections. Those numbers are outlined below.

Correlation |
mOPSc |
mwOBAc |
mBBM |

Runs (R) |
0.475092 |
0.479572 |
0.519380 |

As you can see, the calculations using Marcel projections also show BBM total offense formula having a better correlation than either projected wOBA or projected OPS.

You may ask, “Why do you care”? That is a very valid question. Besides the fact that I am a geek and like to analyze things, I was trying to figure out if there was a summative hitting statistic that I could focus on as I approach my fantasy baseball draft. Most of the people in Baseball Manager tend to focus on OPS. When I read the detail regarding wOBA, I thought I might have found the Holy Grail for hitter performance evaluation. And, it does have the highest correlation to actual runs scored when looking at actual season results. To my surprise, the actual total offensive formula used in my own fantasy leagues was better with preseason projections. Thus, I’m going to use the actual BBM formula against my projections on draft day.