Tagged: MORPS

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!

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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.

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 ROTO Draft Tool

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.

2013 MORPS Roto Draft Tool 20130223 (XLS)

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.