RotoValue UserProfile and e-mail Settings

I’ve added a new switch to the UserProfile page at RotoValue. In addition to check boxes which let you control what transactions generate an automated e-mail, I’d added a box to control whether you show your e-mail address to other league members on team roster pages.

Here’s a view of the UserProfile page:

User Profile pageRotoValue can generate e-mail alerting you to league transactions. By default, you’ll get an e-mail about someone adding, cutting, or trading players, as well as e-mail about trade offers and any FAAB bids you may make. In addition, you can turn on e-mail alerting you to lineup changes, but that is, by default, disabled. You can turn off any or all of these by unchecking the relevant box on the UserProfile page.

The sitecan generate a daily e-mail to owners about their teams, including a boxscore of what the players did last night, and a list of any news stories on those players in the past 24 hours. This job usually runs at about 4:00 AM EDT. By default, you’d get both player stats and news, but you can opt out entirely, or ask to get only the boxscores from the above page.

If you’ve checked “Show e-mail to league members, then in addition to seeing your name under your team as an owner, league members viewing your roster, like this:

Roster page

People viewing the roster who are not league members will see neither your e-mail or your name – clicking on the image goes to the same page, but since most readers likely aren’t in that league, they won’t see my name or e-mail.

By default, the site will not show your e-mail at all, and it will only show the name from your profile to people with teams in the same league. So you’d have to opt in to let other league members see your e-mail.

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Minor League Data Now Available on RotoValue!

I’m happy to report that I’m now receiving minor league (and often foreign league) data from the Chadwick Baseball Bureau.

Subscribers to RotoValue Analyst now can see minor league, fall, winter, and many foreign league statistics on RotoValue Player Detail pages. So you now don’t need to go to some other site to find minor league data on rookies, recent call-ups, or, well, pretty much anybody. For example, here’s a page for young Blue Jays’ second baseman Ryan Goins:

RyanGoinsThe league’s level is in parentheses. In addition to the standard A, AA, and AAA classifications, here are some other levels you might see:
Fal – A fall league, like the Arizona Fall League

Fgn – A foreign league, like Korean Baseball Organization or the Australian Baseball League

FgW – A foreign winter league, like the Venezuelan Winter League

FRk – A foreign rookie league, like the Dominican Summer League

Ind – An independent league, like the Northern League or the Atlantic League

Jpn – A Japanese major league, like the Japan Central League

Rk – A rookie league, like the Appalachian League

People probably have seen Masahiro Tanaka’s Japanese stats, but now RotoValue Analyst subscribers can get this data, and more, directly on RotoValue player pages.

RotoValue Analyst is just $10, giving you access to customized pricing for any or all of your RotoValue leagues, in addition to spring training data, customized category weighting. You can by RotoValue Analyst at the RotoValue Store!

 

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Another 2014 Projection Update…

I’ve done another forecast run, incorporating new injury information, like the recent UCL tear for David Hernandez, which unfortunately probably will end his season, and drops the Diamondbacks down a notch in my team totals.

In addition, I’ve been tweaking how I deal with players with very limited MLB data. Now I will consider current year spring training numbers for players with very little MLB experience, in addition to that tiny MLB experience. Alas, I still don’t have minor league numbers to toss into the soup!

The driver for this was what I felt was a very overly optimistic projection for James Paxton, who was showing up at nearly 200 innings. Interestingly, adding this year’s minor league stats to the 24 innings he had last season did little to change his rate stats; indeed Paxton’s FIP actually went down by adding the spring numbers. But his innings dropped a lot based on a more recent run. Why? Because the Mariners have named fellow rookie lefty Roenis Elias as their #4 starter, and the latest depth chart no longer includes Hisashi Iwakuma, who is likely to start the year on the DL. Iwakuma still projects to over 150 IP in my model, but adding a new starter to the mix sops up a lot of the extra innings the model was giving to Paxton before. Indeed newcomer Elias has pitched even better this spring than Paxton. Both are potential sleeper candidates based on their current roles.

Continue reading

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2014 MLB Projections Update

I’ve updated my 2014 MLB projections, incorporating more recent injury and depth chart information into my playing time estimates.

Of course, since the season is two games old for the Dodgers and Diamondbacks, this isn’t entirely “fair” – usually I’d want preseason forecasts to be entirely preseason. But with a full week before other games, I also don’t want to ignore more recent news for owners who have yet to draft. So I’ve settled on a compromise: I’m keeping my original projections visible on the site, but I’ve renamed them from RotoValue to RV Pre-Australia. Those numbers were my last run before any games started.

But I’ve rerun the numbers again, and am giving these newer number the label “RotoValue”.  I avoided doing any injury updates affecting Dodgers or Diamondbacks, but did incorporate adjustments for other players (like young Rangers’ infielder Jurickson Profar, who is now expected to miss 10-12 weeks). Profar is a good example of this, as on his profile page you can compare these projections. His rate stats don’t change, but where I had projected Profar for 415 AB, I now project just 166.

Here’s an updated standings projection (links to team projections):

AL East Won Lost RS RA
Rays 88 74 710 651
Red Sox 86 76 734 687
Yankees 82 80 687 679
Blue Jays 82 80 732 724
Orioles 78 84 704 730
AL Central Won Lost RS RA
Tigers 92 70 743 653
Indians 79 83 704 718
White Sox 76 86 669 708
Royals 76 86 676 717
Twins 67 95 643 759
AL West Won Lost RS RA
Athletics 87 75 722 670
Angels 86 76 724 685
Mariners 85 77 694 663
Rangers 84 78 726 702
Astros 76 86 674 720
NL East Won Lost RS RA
Nationals 91 71 703 620
Braves 85 77 699 666
Marlins 79 83 641 662
Phillies 76 86 642 686
Mets 75 87 656 707
NL Central Won Lost RS RA
Cardinals 91 71 722 640
Reds 86 76 695 651
Brewers 82 80 697 690
Pirates 78 84 657 685
Cubs 67 95 596 715
NL West Won Lost RS RA
Dodgers 87 75 676 626
Giants 80 82 635 645
Diamondbacks 78 84 668 696
Rockies 77 85 693 729
Padres 76 86 635 674

These are largely the same as my previous projections. Now the Mariners slide to 3rd in the AL West, which still projects as the closest race, with four teams separated by three games, but the Tigers, Cardinals, and Nationals still project to have the most wins, while the Cubs and Twins project to have the fewest.

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Going 9 Expert Draft

Last night I took part in the Going 9 Experts League draft. We started late (11:00 PM EDT), but the event was fast and fun. We drafted a total of 300 players in under 140 minutes, so it went much faster than my auctions do.

The league is a 5×5 12-team mixed league, drafting a total roster of 25 players per team, with 20 active players and a 5 person bench. I tracked the draft on my site, and the full configuration is here. Continue reading

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Inferring Conditional Probability from the FiveThirtyEight NCAA Model

Nate Silver is live with his new FiveThirtyEight, and it being the start of the tournament, I looked at their model for help in filling out my own bracket. It’s quite a slick presentation, and you can see the probabilities of any team reaching any stage of the tournament.

That’s all good! From that page, there’s a link to show data in a table, instead of the large bracket, and when you mouse over the table, they’ll show percentages to 3 decimal places. Yes, that’s up to 5 significant figures (well, it’s 5 figures; I doubt more than 2 are really significant!). That’s cool from a geeky perspective (I should know: I do something similar when I’m displaying projected stats, showing 2 decimal places for integer values), but it’s implying precision that any predictive model really doesn’t have. And it’s inviting scrutiny of that extra precision.

Continue reading

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Polishing the Crystal Ball…

I’ve been hacking on my projections model, and now have a first cut for 2014 available.

In the process of projecting wins and saves I look at projected runs scored and allowed for each team, so in the table below I just take those projected runs and convert them to wins and losses. My model currently does not take defense into account, but I am trying to project playing time based on both a player’s own history and his position on the team’s current depth chart (which also is a major factor in allocating saves).

So without further ado, here’s how my model sees the 2014 MLB season:

AL East Won Lost RS RA
Rays 89 73 707 642
Red Sox 87 75 730 683
Yankees 82 80 683 674
Blue Jays 80 82 729 742
Orioles 77 85 696 734
AL Central Won Lost RS RA
Tigers 92 70 742 652
Indians 78 84 700 722
White Sox 76 86 664 702
Royals 76 86 673 715
Twins 65 97 640 777
AL West Won Lost RS RA
Athletics 87 75 702 655
Mariners 86 76 693 655
Angels 84 78 721 692
Rangers 83 79 715 698
Astros 73 89 671 735
NL East Won Lost RS RA
Nationals 92 70 691 598
Braves 87 75 699 647
Phillies 79 83 661 678
Marlins 78 84 641 663
Mets 76 86 650 690
NL Central Won Lost RS RA
Cardinals 93 69 720 616
Reds 85 77 671 635
Brewers 83 79 696 681
Pirates 80 82 651 658
Cubs 66 96 594 722
NL West Won Lost RS RA
Dodgers 86 76 652 607
Giants 81 81 633 632
Padres 78 84 641 666
Diamondbacks 77 85 664 700
Rockies 74 88 676 736

Click on a team name to see the individual projections for players on that team at the RotoValue site.

My model sees the Detroit Tigers as the AL’s best team, and they also have the weakest division competition, as their 92 wins put them 14 wins ahead of the runner-up Indians. The other AL divisions project to have tight races, with the Tampa Bay Rays 2 games ahead of the world champion Boston Red Sox, and the Oakland Athletics projecting just 1 game ahead of the revamped Seattle Mariners. Yes, that one really surprised me also. Sure, they added Robinson Cano, but there must be something else going on to cause them to surge so much. One thing that stood out to me was my model’s quite aggressive projection for rookie James Paxton, currently pegged as their #4 starter. Paxton pitched wonderfully in four starts last September, but I think my model is indeed projecting him too aggressively, and quite likely the Mariners team as well.

The AL West does rate as the most competitive division, with the Angels and Rangers also within 4 games of the leading Athletics. Those two teams, along with Seattle and Boston, project to fight it out for the two wild card spots.

By contrast, each NL division has a clear projected leader at least 5 games ahead of other teams. St. Louis’s 93 wins top my projections, and give them a comfortable 8 game lead over Cincinnati. Washington projects for 92 wins, 5 games ahead of Atlanta, while the Dodgers’ 86 wins are 5 ahead of the Giants in the NL West.

The Braves and Reds are the projected wild-card teams, with the Brewers, Giants, and Pirates chasing them.

These win totals should be considered an over-under for each team. The best team will almost assuredly win more than the 93 games I project for St. Louis, and the worst will win fewer than the 65 I see for the Twins. Tom Tango wrote a good explanation of this nearly a decade ago talking about individual players, but the same thing applies to team projections as well.

I’ve tinkered with my model this year, regressing offensive players by position rather than overall, and taking into account team depth charts in my algorithm to project playing time, and I still adjust team totals so that they’re reasonable. This is one more reason why James Paxton’s projection was so optimistic: before that last playing time adjustment, the Mariners did not have enough IP from their pitching staff, and since Paxton had better projected rate stats than most other pitchers, he got  a larger boost in playing time.

I’ll continue tweak these projections further, incorporating newer injury information and updated depth charts in the model until the start of the season. You can always get the projections here:

http://www.rotovalue.com/cgi-bin/Search?year=2014&league=4&source=RotoValue

And if you’d like to download the data, the link on the top right corner of the table, saying how many players are found and shown, will download a .csv file of all the data.

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Expanded 2013 wOBA Projections Comparison

7 February 2014: I found a bug in the program that generated the second table, the one using wOBA – 0.020 for any players not forecast, so I’ve replaced the older table with a corrected version, and adjusted some of the other text to reflect that.

I’ve just posted a comparison of nearly 20 different projection sources, using batting average for offense. I prefer a more comprehensive metric, and used Tom Tango’s wOBA in my earlier analysis.

Unfortunately the data Dr. Larson’s site had was not sufficient to compute wOBA for many systems, so to include more systems, I went with the lower common denominator. Many fantasy leagues care about HR and average, but not (directly) 2B, 3B, or (often at all) BB. Certainly a real baseball team cares about more detailed offense.

Much of the extra data from Dr. Larson does let me compute wOBA, as does the Oliver data Brian Cartwright shared, so this post runs the same analysis for wOBA, using only those sources where I have the data to compute it. If you’re reading this and your source was in my last post but not this one, send me more detailed data (to geoff at rotovalue dot com) and I’ll update this post to include your system.

The sources I’m using here are:

Continue reading

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Monster 2013 Projection Review

Update 7 February 2014: I’ve updated the post below to add an additional source, Rosenheck, and to correct a bug in my code generating the table for missing players. The original table incorrectly was computing RMSE and MAE of only the players projected by a system without including missing players at all, leaving a very much apples-to-oranges comparison: systems projecting fewer players tended to project only better players, who are more predictable, and hence those showed much lower RMSE. Now that I actually am including missing points, all systems do have higher errors than in the earlier table, and so the biggest apparent bias was from my own bug. Also, I’ve removed blank projections from BaseballGuru, which caused that to show higher errors in the missing player case, since blank “projections” are for a 0.00 ERA or a .000 batting average.

Tom Tango kindly highlighted my previous posts reviewing the 2013 forecast data that was available on the RotoValue site last year, and in the comments, he pointed me to a data set put online by Will Larson that has 2013 projection data from a dozen sources.

Dr. Larson kindly allows people to write articles using his data so long as they let him know and cite him as the source. I’ve downloaded projections from 12 sources from his site, http://www.bbprojectionproject.com and am including these additional sources in the comparisons for this article:

In addition, Brian Cartwright, the designer of the Oliver projections, shared his 2013 data with me. Thanks, Brian!

Many of these sources have fewer data fields than the other sources I have, with some not providing the raw data from which to compute average, ERA, or WHIP. If a source didn’t provide AB or IP, I used the AB or IP from the consensus of the five sources I had last year. I then computed hits from average for batters, ER from ERA for pitchers, and hits/walks from WHIP (if a source had neither, I simply assumed 3 hits per walk, and derived both from WHIP).

I’m also including the same sources I had before:

In addition, I’m keeping the same Consensus forecast I had previously (a simple average of the 5 sources above), and adding a new AllConsensus, that is a simple average of all 17 systems I’m evaluating here.

As before I’ll be showing two tables per statistic, one comparing only those players projected by all systems, and another assuming a rate value for players not projected by a system relative to that system’s average. Since there’s not enough data to compute wOBA for each source (or SLG, or OBP), I’m just going to do batting average on offense here, and I’ll subtract 0.020 from the source’s overall average for any missing players.

For pitching, I’ll be able to do ERA and WHIP for each source, adding 0.50 to average ERA and 0.10 to average WHIP for missing players.

I’m still computing MAE and RMSE for each source, with bias-adjusted errors. A source is graded by how well it projects players relative to its own average, not by how well its average wins up matching the actual average. For people using projections in fantasy analysis, this is what you’d want: the player’s ranking depends on their relative, not absolute, statistics.

So let’s get to the numbers! Continue reading

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2013 Projection Systems Review: RA9 and WHIP

Thursday I compared five projection systems with their projections for weighted on base average. Today I’m looking at two different pitching categories, runs allowed per 9 innings (RA9) and WHIP, walks plus hits per innings pitched.

Tom Tango kindly highlighted my post yesterday, but suggested that one of my charts was useless, because it did not compare the systems on the same players. So today I’ll just have two charts per stat, one using only those players projected by all systems, and another filling in a “missing” value for any player a system did not project.

The systems are the same five:

And, as before, I’m calculating RMSE and MAE, and sorting by the former. The error is bias-adjusted, so I’m first comparing each player’s stat to the average of the system, and then I compare those deltas with the actual delta. I’m using actual innings pitched to weight the averages. First up, RA9.

Continue reading

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