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.
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.
The 2016 MORPS baseline projections are ready. This is the third year we have provided baselines. These projections use all the models we have put together over the years for projecting player performance. This means that the projections are still based on four years of data, positional mean regression, etc. However, they do not account for a player changing positions, reductions in playing time, new players to the big leagues, etc. We entered all MLB player transactions into system since the end of the regular season last year. While this doesn’t guarantee that we have caught every trade, free agent move or player being waived; we are hoping that the majority of these type of transactions were captured in the system.
Some may like the baseline projections more than the final version. I read one review of MORPS in 2014 that criticized the fact that we took the time to model anticipate plate appearances and batters faced for each team before releasing our final projections. They didn’t consider that process “scientific”. Our perspective is that the modeling allows us to adjust the ratios between each stat and plate appearance or batter faced to account for situations that weren’t present the year before. This could be a player being part of a platoon when they played the position full-time the year before. It could be a reduction in playing time due to the appearance of a blockbuster free agent or anticipated rookie hitting the big leagues. It could also be a pitcher coming back from Tommy John surgery after being out of the game for over a year. Regardless of the situation, we believe that the modeling of plate appearances and batters faced for each team adds significant value to MORPS projections. This view is supported by our #1 ranking in 2014 for player projections using root mean square error (RMSE). If you still doubt our ability to accurately model these situations or you have an early fantasy draft and need something now, you’re in luck. You can use our baseline projections.
So…. without further ado, we present the 2016 MORPS Baseline Projections. The Batting and Pitching projections are available in excel and PDF formats. Follow the links below to download your copy.
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 re-sort the XLS spreadsheet in RC order for batters and OERV order for pitchers. Play Ball!
Most opening day rosters are set which means its time for the final 2014 MORPS projections. If you’re a Braves fan, you have to be wondering why your city is cursed. First its the traffic jam to end all traffic jams. Next, it’s all your pitchers getting hit by the injury bug. I’m hoping that some minor adjustments this year will yield even better results for this year’s projections. We’ll check in October to see how the numbers mapped to real stats.
For those who play Fantasy, remember to sort your stats for your scoring system. Baseball Manager (BBM) managers should sort batters by Runs Created (RC) and pitchers by OERV. This should yield the best results for simulation leagues that use real stats for nightly scoring. Roto leagues should use the projections as presented below.
MORPS updates for pitchers and batters are as follows:
Feel free to leave comments or suggestions.
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:
Feel free to leave comments or suggestions.
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.
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.
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.
The latest MORPS updates are now incorporated into the 2013 MORPS Roto Draft Tool. The tool was updated found within the first posting at the following link – click here.
One major change was an update to how the ROTO value and ROTO RANK are calculated. I found in a number of my drafts that MORPS projections were suggesting picks like Mark Reynolds and Adam Dunn well before other lists on the market. Players were getting “points” for number of homers, runs, and batting average; however, I was not showing the negative impact that a player could have with a rate stat like batting average. Adjustments were made to the formulas. Players with low batting averages will now show negative impact on your fantasy team as well as their positive impact within certain counting stats.
I came across an excel tool last year from Razzball that automated much of the draft process for ROTO leagues. I modified it with MORPS projections and added a bit more functionality. It worked well for my ROTO draft last year. Thus, I decided to use it again this year. I also decided to share the modified tool this year with MORPS followers.
Take time to check the instructions page. It highlights what needs done to complete your preparation. The User Input page allows you to customize the tool for your own league, goals, etc. The only other page you will need to alter in any way is the Players page. During the draft, you update players taken on this page with a drop down team selection that uses the teams you entered on the User Input page. Players are automatically marked as taken in the dashboard by stat and the dashboard by position. This allows you to see next available players based upon position or any of the standard 5×5 roto stat categories. The War Room is where all the player draft data is consolidated together to give you a running overview of your team and the other teams in your league.
Feel free to make suggestions for improvement. Hopefully everyone else finds it as useful as I did with my own drafts.