Tagged: baseball

2016 MORPS Team Projections

MORPS 2016 team projections posted below.  2015 team projections only picked 50% of the playoff teams which is better than the 2014. We definitely missed on the Kansas City Royals and their World Series win.   On the positive side, we did predict playoff runs for the Mets, Cardinals, Dodgers, Pirates and Blue Jays.  Our only miss in the NL was the Cubs over the Nationals.  The AL is another story.  In addition to the Royals, we also missed on the Astros, Rangers, and Yankees.  Predicting 50% of the playoff participants isn’t bad considering the number of roster changes that happen during the course of the season.  Between 2013 and 2010, MORPS team projections averaged 73%.  We are hoping move in that direction this year.

This year’s team projections are as follows:

American League

2014 AL East

Wins

Losses

Toronto

90

72

Boston

83

79

Tampa Bay

81

81

Baltimore

78

84

New York

75

87

2014 AL Central

Wins

Losses

Cleveland

88

74

Detroit

83

79

Chicago

80

82

Minnesota

77

85

Kansas City

73

89

2014 AL West

Wins

Losses

Seattle

87

75

Houston

86

76

Texas

82

80

Los Angeles

78

84

Oakland

77

85

 

National League

2014 NL East

Wins

Losses

New York

97

65

Washington

87

75

Miami

81

81

Atlanta

67

95

Philadelphia

64

98

2014 NL Central

Wins

Losses

Chicago

92

70

Saint Louis

84

78

Pittsburgh

82

80

Cincinnati

75

87

Milwaukee

74

88

2014 NL West

Wins

Losses

Los Angeles

95

67

San Francisco

88

74

Arizona

81

81

San Diego

73

89

Colorado

71

91

The Division winners in the NL are New York, Chicago, and Los Angeles with Washington and San Francisco slipping in as the wild card teams.  The division winners are certainly not a surprise nor are the wildcard teams.  The American League Division winners will be Toronto, Cleveland, and Seattle with Houston as a wildcard team.  Boston and Detroit will play a one game playoff to determine the last wildcard spot.  Unlike the NL, MORPS predictions in the AL will be s surprise to most.  USA Today has Cleveland finishing third and Seattle fourth.

While the team with the most wins don’t always do that well in the playoffs, such distinctions can’t be made with a projection system built around “Runs Created” and “Runs Allowed”.   MORPS is projecting an AL championship between Cleveland and Toronto with Toronto going to the world series.  In the National League it will be Los Angeles versus New York with the Mets going to the world series.  MORPS projects that the New York Mets will win the series in 6 games.

Those that want more information on the projection methodology can click here.

As always, feel free to post your comments.

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!

Final 2014 MORPS Projection Updates

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:

2014 MORPS Batting Projections 20140327 (XLS)

2014 MORPS Batting Projections 20140327 (PDF)

2014 MORPS Pitching Projections 20140327 (XLS)

2014 MORPS Pitching Projections 20140327 (PDF)

Feel free to leave comments or suggestions.

2014 MORPS Roto Draft Tool

I came across an excel tool two years ago 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 drafts the last two years.  Thus, I decided to use it again this year.  I also decided to share the modified tool this year with MORPS followers.

2014 MORPS Roto Draft Tool

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.

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.

2013 MORPS Team Projections

2013 MORPS MLB Team Projections are outlined below.  Unlike last year, I am not going to introduce the projections one team at a time.  One advantage of moving to a relational database is that the formulas, once applied correctly, are available for all teams in all divisions.

American League

2012 AL East

Wins

Losses

Boston

87

75

New York

87

75

Toronto

84

78

Tampa Bay

80

82

Baltimore

72

90

2012 AL Central

Wins

Losses

Detroit

96

66

Chicago

82

80

Kansas City

81

81

Cleveland

74

88

Minnesota

71

91

2012 AL West

Wins

Losses

Los Angeles

91

71

Texas

86

76

Oakland

81

81

Seattle

77

85

Houston

64

98

 

National League

2012 NL East

Wins

Losses

Atlanta

88

74

Washington

87

75

Philadelphia

85

77

New York

77

85

Miami

68

94

2012 NL Central

Wins

Losses

Cincinnati

88

74

Saint Louis

88

74

Milwaukee

84

78

Pittsburgh

74

88

Chicago

70

92

2012 NL West

Wins

Losses

San Francisco

88

74

Los Angeles

86

76

Arizona

83

79

Colorado

79

83

San Diego

72

90

Projections were somewhat easier this year because all divisions in both leagues have the same number of teams.  This means that each team plays the same number of games within their respective divisions and leagues as well as the same number of inter-league games.  This does not mean that the competition that each team plays is the same.  Some divisions, as always, are stronger than others.

I must admit that my projections were a surprise.  They certainly don’t align with the messages I am hearing on major talk radio shows over the last month.  No one has Boston on top of the American League East.  Their pitching staff is projected to be one of the five worst in the American League.  However, their offense is projected to be the best in the majors.  One team that has received a lot of attention in recent weeks is the Cleveland Indians.  Their offense is certainly going to be better than last year, but their starting pitching is projected to be the worst in baseball.

Projected division winners in the American League are Detroit, Los Angeles, and Boston.  New York and Texas are projected to be the AL wild card teams.  The National League division winners are Atlanta, San Francisco and Cincinnati.  The NL wild card teams are Saint Louis and Washington.  I found it interesting that four National League teams have an equivalent projection of 88 wins.  Unlike the American League, the National League doesn’t have any run away division winners.

Those that want more information on the projection methodology can click here.

As always, feel free to post your comments.

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