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 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 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.

2014 MORPS Team Projections

MORPS 2014 projections will be ready later this week.  While everyone is waiting, I thought some might enjoy reading the MORPS Team Projections for 2014.  In 2013, MORPS picked 4 of the 6 division winners based upon projected wins and losses.  This included Boston, Saint Louis and Detroit.  Of the four teams that went to a championship series, only the Dodgers were not in the MORPS playoff projections.  They missed on that projection by one whole game.

This year’s team projections are as follows:

American League

2014 AL East

Wins

Losses

New York

89

73

Toronto

89

73

Tampa Bay

87

75

Boston

78

84

Baltimore

70

92

2014 AL Central

Wins

Losses

Detroit

93

69

Kansas City

83

79

Chicago

80

82

Cleveland

78

84

Minnesota

66

96

2014 AL West

Wins

Losses

Texas

82

80

Seattle

82

80

Houston

81

81

Los Angeles

79

83

Oakland

79

83

 

National League

2014 NL East

Wins

Losses

Washington

85

77

Atlanta

83

79

Philadelphia

81

81

New York

79

83

Miami

75

87

2014 NL Central

Wins

Losses

Saint Louis

91

71

Milwaukee

88

74

Cincinnati

80

82

Pittsburgh

74

88

Chicago

64

98

2014 NL West

Wins

Losses

Colorado

98

64

Los Angeles

86

76

San Diego

81

81

San Francisco

77

85

Arizona

74

88

3/27/2014 update – Roster changes and injuries have helped some teams and hurt others over the course of Spring Training.  The team projections have been updated to reflect current team rosters and player projections.  Atlanta’s pitching injuries have dropped them in the standings and elevated the Washington Nationals to NL East Division winners for 2014.  Toronto and Tampa Bay have distanced themselves from the rest of the AL pack for wild card spots.  The rest of the projected division winners and wild card projections remain the same – New York, Detroit and Texas as division winners in the AL, Saint Louis and Colorado as the other NL division winners, and Milwaukee and Los Angeles with the wild card spots in the NL.

2/25/2014 Original Post – Each year the  numbers surprise me because they rarely agree with the talking heads on popular sports talk shows around the country.  This year is no different.  Division winners this year include New York, Detroit and Texas in the AL while Atlanta, Saint Louis and Colorado will represent the NL.  The wild card in the AL is going to come down to the wire.  MORPS is projecting the first wild card to be Tampa Bay while the second wild card is a three-way tie between Seattle, Kansas City, and Toronto.  Can you say multiple play in games – wouldn’t that be exciting.  The wild card race in the NL is a little more straight forward with Milwaukee and the Dodgers distancing themselves from the rest of the pack.  

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

As always, feel free to post your comments.

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!

2013 MORPS Roto Draft Tool Update

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.

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”.

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.

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