2014 Fantasy All Stars

It’s easy to find lists of the best players in fantasy baseball. Lots of people have opinions on the matter, and plenty use formulas and models to rank players. Clayton Kershaw is baseball’s best starter, and was likely the most valuable pitcher in almost any format. Mike Trout was also outstanding, and while not clearly the best position player for 2014 (in some formatsJose Altuve or Victor Martinez might be worth a little more), he’s a top 3 pick in any draft format.

Today I want to highlight players who exceeded expectations the most. Sure, Trout and Kershaw are great players, but to own them you needed to have a top draft pick, or spend an awful lot of your salary cap at auction, or make an overwhelming trade offer to their owners. Owning a player expected to earn $10 but who winds up earning $20 or more has a much bigger impact on winning your league.

So the most valuable fantasy players are those who produce much more than expected, providing first round value with a mid or late round pick. To highlight such players in 2014, I’m comparing RotoValue prices computed using 2014 actual statistics with those computed using preseason projections, and for this article, I’m using Steamer’s highly regarded projections as input. For dollar values, I’m using a 12-team 5×5 mixed league with an active roster of 8 pitchers and 10 batters. The full description of that league is here, and I describe the basic model to convert statistics to prices given the configuration of a league here.

Player Team Position Actual 2014 Diff
Michael Brantley Indians CF/LF $40.50 $-1.80 $40.50
Victor Martinez Tigers 1B/DH $40.19 $2.84 $37.35
Johnny Cueto Reds SP $46.35 $11.28 $35.07
Corey Kluber Indians SP $38.29 $3.66 $34.62
Nelson Cruz Orioles DH/LF/RF $36.11 $3.43 $32.68
Anthony Rendon Nationals 2B/3B $32.11 $-7.54 $32.11
Todd Frazier Reds 1B/3B $29.96 $-7.50 $29.96
Charlie Blackmon Rockies CF/LF/RF $26.48 $-33.84 $26.48
Jose Altuve Astros 2B $38.84 $13.87 $24.97
Brian Dozier Twins 2B $24.41 $-3.99 $24.41
Dee Gordon Dodgers 2B/SS $24.17 $-23.09 $24.17
Corey Dickerson Rockies CF/LF $22.41 $-14.29 $22.41
Jon Lester Athletics SP $29.76 $7.99 $21.77

Diff is the lesser of the player’s actual RotoValue based on his 2014 stats and his RotoValue based on 2014 minus his RotoValue based on the preseason Steamer forecasts: after all, if you forecast a player to have negative value, you wouldn’t start him. So his actual value is that above 0, not whatever negative price a forecast might imply. My table shows all players who earned at least $20 more than their Steamer projected stats were worth.

Michael Brantley is the fantasy MVP by this calculation, as he earned $40.50 this year despite being projected to be below replacement by Steamer (-$1.80). Steamer projected a mediocre line of .272, 8 HR, 61 R, 55 RBI, and 13 SB, which ranked 68th among players who eventually qualified at outfield. For a league with 36 starting outfielders, an 24 starters at “any” position, that’s below replacement level. But Brantley, who turned 27 in May, posted career bests across the board, batting .327 with 20 HR, 94 R, 97 RBI, and 23 SB (with just 1 CS). Brantley was the second-best position player outright, behind Mike Trout.

Not far behind is Tigers’ DH Victor Martinez (.332, 32, 87, 103, 3 actual, .289, 13, 66, 69, 2 projected). Steamer projected a continuing decline for the 35-year old former catcher, but he instead posted career bests in average and HR. Martinez was the third most valuable batter, period. Steamer was less optimistic on Martinez than my RotoValue projections, which had him worth $16.26 (.295, 16, 69, 88, 1), but Martinez still had a much better year than any projections.

Johnny Cueto was the best pitcher by this metric, and second-best behind Clayton Kershaw overall, as he earned $46.35. Cueto led the NL in innings pitched and strikeouts (242), and ranked second to Kershaw in ERA (2.25), WHIP (0.960), and wins (20). His Steamer projections (1.251, 3.74, 146 K, 11 W) were worth just $11.28. Cueto not only stayed healthy all year after missing most of 2013, he also struck out nearly a batter an inning, by far his highest rate in the big leagues.

Indians’ starter Corey Kluber was almost as big a surprise to Steamer as Cueto. Projected for 1.299, 4.07, 7 W, 146 K, Kluber had a breakout year, boosting his strikeout rate over 10 per 9 IP while posting career best percentages of 1.095 and 2.44. His 18 wins tied the more heralded Max Scherzer and Jered Weaver for the AL lead, while his 269 strikeouts trailed only David Price in all of MLB. While his WHIP far behind Cueto’s, resulting in a lover overall RotoValue, the two pitchers beat their Steamer projected prices by about $35 each, by far the biggest pitching surprises.

Before this season Nelson Cruz only had one season where he played more than 130 games, or had more than 500 AB. While he had always shown great power, he had not put together the monster season he seemed capable of. He was on pace last season before a drug suspension ended his year 50 games early. Steamer projected .253, 24 HR, 63 R, 71 RBI in 466 AB, while my RotoValue projections were quite similar: .258, 21, 56, 71 in 433 AB. And while the per AB rates were good, Cruz was projected to miss at least a quarter of the season, depressing his value because of lower cumulative category totals.

This year, however, he put it all together, leading the majors with 40 HR in 613 AB, and ranking 4th with 108 RBI, while batting .271 with 87 runs and 4 SB. His fantasy year basically matched Miguel Cabrera, as he hit 15 more HR to compensate for fewer runs scored and a lower batting average.

As with Cruz, projections for Anthony Rendon did not anticipate a full season: Steamer projected 448 AB, 11 HR, 55 R, 52 RBI, and 3 SB, numbers which resulted in a negative RotoValue in this format. Rendon matched Cruz’s 613 AB while hitting .287 with an NL-leading 111 runs scored, 21 HR, 83 RBI, and 17 SB. His $32.11 RotoValue was highest among regular third basemen (Miguel Cabrera played 10 games at 3B this year, and might have qualified there in some leagues), while he also likely qualified at 2B. He had little minor league track record, playing just 101 games for 6 teams in parts of 2 seasons, but in that small sample size of 337 AB he showed excellent offensive potential, with a .407 cumulative wOBA. 2014 was probably the last chance to get Rendon cheaply for quite some time.

Todd Frazier had shown power but struggled with getting on base before this season. Steamer projected those trends to continue: .241, 18 HR, 55 R, 61 RBI, 6 SB, numbers which, like Rendon, did not project to be worth owning in this format. Instead, Frazier had a stellar year, posting career bests in all five fantasy categories: .273, 29 HR, 88 R, 80 RBI, and 20 SB. Perhaps much of Frazier’s breakout was bad luck reversing itself, as his BABIP rose from .269 last year to .309 in 2014. But he also added value in steals. While he hadn’t run much in the majors before, Frazier did go 17-21 in steal attempts in just 90 games at Louisville in 2011. His fantasy owners surely appreciated the attempts.

Charlie Blackmon was not even expected to start in the crowded Rockies outfield last spring. Steamer projected him to hit .270 in just 162 AB, with 3 HR, 20 R, 18 RBI and 4 SB. Instead, a torrid April turned him into an every day player, with an expected rise in his cumulative totals. Blackmon hit .389 in April, with 5 HR, 23 RBI, and 7 SB, good for a $54.03 RotoValue for the month, the best in baseball. He cooled off considerably after that, but his year-end totals of .288, 19 HR, 82 R, 72 RBI, and 28 SB were still worth $26.48. Quite nice for someone who wasn’t expected to start! But in practice, I doubt too many owners actually had him in their lineup for all of his hot start. From May 1st onward, he hit .271, 14 HR, 59 R, 54 RBI, 21 SB in 494 AB, good for a $17.63 RotoValue. That’s still a nice return for a player probably unowned after most drafts last year.

Steamer expected Jose Altuve to be a good player: .281, 8 HR, 74 R, 59 RBI, 29 SB, numbers worth $13.87. That’s a solid, productive middle infielder. Instead, Altuve had a huge fatasy year. He led the majors in average at .341, and added 7 HR, 85 R, 59 RBI, and an AL-leading 56 SB, making his year worth $38.84. In some formats, Altuve was worth more than Mike Trout. While he doesn’t have much home run power, Altuve did hit 47 doubles this year. One mild caution sign on an otherwise stellar year: he probably got a little lucky on balls in play. His 2014 BABIP was .360, about 40 points higher than his 2012/2013 averages. If he reverts to those values, his batting average would drop back, which would also reduce his opportunities to steal.

Brian Dozier’s line is perhaps the most surprising of all in this list. Steamer projected .245 with 11 HR, 58 R, 50 RBI, and 12 SB in 481 AB, numbers which would give a slightly negative RotoValue. That forecast actually was spot on in batting average, as Dozier hit .242. But instead of 481 AB, he nearly reached 600, and he posted much better cumulative stats: 23 HR, 71 RBI, 21 SB, and an amazing 112 runs scored, second only to Mike Trout in all of MLB. So he showed more power than projected, and ran more, but the bigger change was in his walks. Steamer projected just 38 BB, and a .303 OBP, numbers in line with his first two big-league campaigns. But Dozier actually walked 89 times, giving him a good .344 OBP despite the low batting average. He still scored more runs than I’d expect just from his OBP, but those extra walks made the run total less shocking. Dozier hadn’t shown much power in the minors (just 19 HR in over 1500 AB over four seasons), but he’s now got 41 HRs over his last two big league seasons. He’s become a much better fantasy player than his minor league numbers would have suggested.

Dee Gordon has long tantalized fantasy owners with the promise of lots of steals from the middle infield. In 2011 he seemed a budding star, batting .304 with 24 SB in just 56 games, but he struggled the next two years, spending more time in AAA Albuquerque than in the majors. So his 2014 projections were weighted down by those two poor years of MLB performance. Steamer projected Gordon for 250 AB, a .247 average, and 21 SB, well below a replacement level fantasy player. But in 2014, Gordon won the starting SS job in the spring and fulfilled his long-expected promise, batting .289 with 2 HR, 94 R, 34 RBI, and an MLB-best 64 SB. His BABIP, which was under .300 in the past two seasons, was .346 this year, so while his fantasy owners greatly enjoyed 2014, if the BABIP regresses, he might not be so fun to own next year. Also, despite an average more than 50 points higher than Brian Dozier’s, Gordon’s OBP of .325 was lower, as he walked just 31 times.

While Charlie Blackmon got the early-season attention for flirting with a .400 average last April, Corey Dickerson rather quietly had a fine season himself in the Rockies outfield. Steamer projected Dickerson for more playing time, but still not a full regular job: .278, 10 HR, 40 RBI, 37 R, 7 SB in 281 AB, not enough playing time to have positive RotoValue in this league. Dickerson didn’t play as much as Blackmon, but he was almost as valuable, batting .312 with 24 HR, 74 R, 76 RBI, and 8 SB in 436 AB. Given that Dickerson is three years younger, hit better in the minors, and on a per-AB basis had a better year in 2014, I’d rather own him next season than Blackmon. Depending, of course, on the price!

The final player on the list is Jon Lester. Prior to this season, it seemed like the now-30 year old Lester’s best years were behind him. He had bounced back from a career-worst year in 2012 to a little worse than his early career averages – a good, but not great, starter. Steamer projected him for a 4.01 ERA, 1.323 WHIP, 13 wins, and 156 Ks in 192 IP, numbers a little worse than 2013, but much better than 2012. That seemed reasonable (my own projections were eerily similar: 4.04, 1.335, 12, 170 Ks in 205 IP). Instead, seemingly out of nowhere, Lester pitched like an ace this year: 2.46, 1.102, 220 Ks, 16 W, earning $29.76. His was the 7th best season among MLB pitchers in this fantasy format – for all practical purposes tied for 6th with David Price ($29.82; 3.26, 1.079, 271 K, 15 W). The big change, it seems, was improved control: in past years Lester walked 3-3.5 batters per 9 innings; in 2014, he cut that to 2, the lowest rate by far of his career. His strikeout rate got back above 9 for the first time since 2010. The trade to Oakland has him in a better pitchers’ park, although if anything he was a little more effective before the trade than after it this year. In any event, Lester projected to be an $8 pitcher, but wound up as a $30 pitcher.

In compiling this list I was at first surprised that there were no relievers at the top. Every year someone projected as a middle reliever or set-up guy winds up as a closer and racks up plenty of saves, and I initially thought such a player would make the list. But I’m used to playing a 4×4 format, where saves is one of just 4 pitching categories. These rankings are based on a 5×5 format with strikeouts, and relievers simply don’t pitch often enough to rack up high strikeout totals. So relievers are worth less in this format than in 4×4. Francisco Rodriguez was projected to be not worth owning by Steamer in this league (3.57, 1.242, 4 W, 5 SV, -$9.07), but wound up with the Brewers’ closing job, earning $14.87 (3.04, 0.985, 5 W, 44 SV). His season ranked 3rd among relievers behind only Greg Holland and Craig Kimbrel.

The other positions not represented in the table above were first base and catcher. Mets 1B Lucas Duda just missed the list, as his year earned $19.91 despite being projected to be unowned, while Devin Mesoraco earned $19.21 while projecting to be a bench player.

If you had several of these players on your fantasy team, you probably had a very good year overall.

By contrast, if you had Prince Fielder, Cliff Lee, or Carlos Gonzalez you probably had a tough year.

Thanks to Jared Cross for making Steamer data available on RotoValue. I still believe that projections, and Steamer is among the best at it, are the best way to value talent for an upcoming fantasy season. It’s inevitable that some players will outperform projections by quite a bit. If you knew in advance who would do that, you’d be win win your league!

Posted in Auction, Draft, Major League Baseball, Projections, RotoValue | Comments Off on 2014 Fantasy All Stars

Adding More MLB Statistics

I’ve added some more basic statistics for baseball to the RotoValue system.

For pitchers, I’ve added 2B allowed, 3B allowed, runs allowed, balks, and at bats against.

For batters, I’ve added games started and intentional walks.

You can chose these as scoring and/or (if you’re a RotoValue Analyst customer) display categories.

Posted in Major League Baseball, RotoValue | Comments Off on Adding More MLB Statistics

RotoValue Now Uses RotoWire Player News

The player news and analysis notes shown on RotoValue now come from RotoWire instead of RotoWorld.

My feed provider for news, XML Team Solutions, contacted me Friday to let me know they had lost the ability to send RotoWorld data, but were now sending RotoWire instead. The RotoWorld data stopped coming in on Thursday, July 31st 2014, and RotoWire data started coming in on Friday, August 1st. In addition, XML Team sent older RotoWire notes, data from Wednesday July 30th onward, so the gap without news updates was filled by newer data.

Posted in Major League Baseball, NBA Basketball, NFL Football, RotoValue | Comments Off on RotoValue Now Uses RotoWire Player News

Who gains and loses if we #RedefineTheWin?

Wednesday I introduced a page comparing traditional wins and losses with those assigned by a points system proposed by Tom Tango. Tom agreed with my modification of run values, using -5 in win points and 10 in loss points.

Today I’d like to highlight some of the biggest changes in record using this proposal would have produced in recent years.

In 2013Tyson Ross had an official record of 3-8, but his Tango record improves 8 games to 8-5, the biggest improvement in the majors in 2013. Ross was pulled from the rotation early in the year, but rejoined it in late July, giving up 3.67 runs/9 innings over 125 innings (16 starts and 19 relief appearances).

Ubaldo Jimenez (3.70 R/9) improved by 7 games, from 13-9 to 18-7, which seems reasonable given that his team won so many of his starts. Phil Hughes also improved 7 games, but from an awful 4-14 to a simply bad 7-10, which I suppose somewhat better reflects his poor, but not hideous, 5.62 R/9. Chris Sale reversed his actual 11-14 record to 14-11, a 6 game improvement, but still suffered from rather poor run support, as he had a 3.40 R/9 and an excellent 1.068 WHIP.

The two pitchers hurt the most in 2013 were both relievers. Bryan Shaw officially went 7-3, and his rate stats were still good (3.72 R/9, 1.173 WHIP), but his Tango record dropped 7 games to 2-5. Luke Gregerson had even better rate stats (3.26 R/9 and 1.010 WHIP), but his record also dropped 7 games, from 6-8 to 1-10.

Four pitchers saw their record drop 6 games, with C.J. Wilson the only starter in the bunch. Wilson officially was 17-7, and his ERA, 3.39 seems rather good. But his WHIP was a more pedestrian 1.342, and he gave up 13 unearned runs on the season, so his R/9 was 3.94. His Tango record worked out to a still good 12-8, which I think much better reflects his effectiveness.

Starters gained the most in 2012 also, with Kris Medlen improving 9 games from 10-1 to 18-0. Medlen actually started the year in relief, not joining the rotation until late July. He won 9  of 12 starts under the official rules, but under the Tango proposal, he’d be credited with wins in all 12 of his starts, as well as an extra 5 relief appearances. His sole loss, in relief on May 26th, would now go to starter Mike Minor, who gave up the first 4 runs in an 8-4 loss. Medlen allowed just 1.70 R/9 and 0.913 WHIP in his 138 IP.

Jordan Zimmermann also improves by 9 games, from 12-8 to 19-6, for a year when gave up 3.17 R/9. Interestingly, in 2013, Zimmerman actually won 19 games under the official rules despite his R/9 rising to 3.42.

On the negative side, relievers again fall the most, with Rex Brothers dropping from 8-2 to 1-7, a 12 game swing. Brothers allowed 4.39 R/9 with a 1.478 WHIP. James Russell fell 10 games, from 7-1 to 2-6 in a year where he gave up 3.63 R/9 with a 1.298 WHIP. The biggest drop for a starter in 2012, 6 games belongs to Wandy Rodriguez, who went 12-13 splitting time between the Astros and Pirates, but has a Tango record of 8-15. Rodriguez gave up 4.33 R/9 and 1.269 WHIP on the year.

2011 saw a bit of a reversal, with two relievers improving the most. Daniel Bard‘s actual record was 2-9, but he went 7-5 under the Tango rule, a 9 game improvement. Bard gave up 3.58 R/9, and just 0.959 WHIP in 70 relief appearances that year. Jim Johnson didn’t become the Orioles’ closer until September, but he saw his actual 6-5 record improve 8 games to 11-2, in a season where he gave up 2.97 R/9 and 1.110 WHIP while throwing 91 innings.

The two biggest drops in record belonged to starters. Derek Holland fell 9 games, from a stellar 16-5 to 12-10, with the latter record much better reflecting his effectiveness (4.41 R/9 and 1.354 WHIP). John Lackey had awful percentage numbers, 6.69 R/9 and a 1.641 WHIP, yet somehow had a 12-12 official record, which falls to 4-16 under the Tango method, again a better reflection of his actual performance.

Amusingly Daniel Bard also topped the 2010 list of biggest improvement, going from a 1-2 official record to 7-0 in a stellar season when he gave up just 2.17 R/9 and 1.004 WHIP. His 8 game improvement was matched by four other players: Mark Hendrickson (1-6 to 6-3), Darren Oliver (1-2 to 11-4), Tommy Hanson (10-11 to 16-9), and Wade LeBlanc (8-12 to 11-7). The biggest fall was 7 games, also by 4 players: Brandon League (9-7 to 2-7), Jeff Weaver (5-1 to 1-4), Bruce Chen (12-7 to 5-7), and Billy Wagner (7-2 to 1-3). Wagner was quite effective overall as the Braves closer that season (1.82 R/9 and 0.865 WHIP), but a one-inning pitcher who is the last in the order isn’t likely to pick up wins. I was somewhat surprised that although Wagner also had 7 blown saves, just 2 of his wins came in blown saves.

Overall these changes seem to make more sense for starters than relievers. Typically those whose records improve a lot pitched well (or at least had a very bad actual record), while those who dropped tended to pitch poorly (or had an unusually good actual record). For relievers the big changes seem more random, but that likely just reflects  the nature of the role. Under the Tango rule, a reliever will only get a win if the starter is relatively ineffective, and then only by being either the most effective reliever, or the one who enters first among those tied. While that is somewhat arbitrary, it does improve over the official rule, where what matters is simply being the pitcher of record when your team scores to take a lead it never relinquishes. I was moderately surprised not to see more closers on the biggest drop list, since they’re likely to lose both vulture wins (when they blow the lead, but their offense wins the game the next inning) as well as wins when they enter tie games (there’s a good chance that a starter, or at least an earlier reliever, was as effective under win points).

The Tango record shouldn’t be used to assess overall pitcher effectiveness, but it does relate pitcher performance to actual team wins and losses, and it is in many cases a significant improvement over the current rule for assigning decisions. You can track the 2014 MLB leaders in Tango wins here.


Posted in Major League Baseball, Sabermetrics | Comments Off on Who gains and loses if we #RedefineTheWin?


While Brian Kenny turned #KillTheWin into a Twitter meme, Tom Tango has proposed redefining it. Rather than using the old definition, he suggests computing “win points” and “loss points” for each game, and then giving the player with the most win points on the winning team a win, while the player with the most loss points on the losing team gets the loss. This is intended as a “descriptive” stat, not an analytic one. It’s based on actual wins and losses, but rather than using the old rule, it tries, via a rather simple formula, to find the most deserving pitcher to get the win or loss.

His original suggestion was to give 1 win point per out recorded, and -4 win points per run allowed (whether earned or not) to each pitcher. Highest total gets the loss. In case of ties, the win goes to the pitcher giving up the fewest runs among tied pitchers; if still tied, then give it to the one getting the most outs; finally, if still tied, go with the one who entered the game first.

Loss points were similar: 6 points per run allowed (again whether earned or not), and -1 points per out recorded. For losses, though, you ignore any pitcher who does not give up a run (you don’t deserve a loss if they don’t score on you!). The tiebreakers there are symmetric: first give the loss to the pitcher giving up the most runs; then give it to the one getting the fewest outs; finally, go with the one who entered the game first.

I’ve created a page on my site that computes Tango wins following these rules for seasons from 2010 onward. Well, not exactly. The page actually lets you try your own coefficients,  after a discussion on Tango’s site about results from the earlier model. So I’ve generalized, and in tinkering I think using -5 points per run allowed for win points, and 10 points per run allowed for loss points, gives a better distribution of wins and losses between relievers. That’s the default I use on the page now. I do notice that the traditional rule has a much more stable breakdown in winning percentage of starters and relievers than the Tango proposal, no matter what coefficients I try.

This seems like a promising alternative to traditional wins and losses. The rules are actually simpler to describe than the present MLB rules, and it looks like it removes much of the luck from the traditional allocation. It is still theoretically possible under this system to get a loss you don’t “deserve”. Suppose the starter pitches in the bottom of the 9th inning of a scoreless game, and strikes out the first two batters, but then walks one. The closer comes in, and gives up a triple to end the game. Since the run allowed is charged to the starter, he takes the Tango Loss, as he’s the only one to give up a run, even though the 2-out triple had more to do with that run scoring than the 2-out walk. But that’s a minor quibble, and if anything its more of an indictment of how runs are allocated when pitchers leave with men on base than of the win proposal anyhow. If the run allowed were charged to the closer in that case, he’d get the loss.

It’s also possible to get a Tango Win in a game where current rules would credit you with a save.

I did try to adjust one of the tiebreakers. The Win Points and Loss Points definitions and even tiebreakers are almost perfectly symmetric: positive coefficients in win points are replaced by negative ones in loss points; most tiebreakers in win points are replaced by fewest tiebreakers in loss points. So I wondered what would happen if I switched the last tiebreaker for loss points, giving the loss to the last pitcher to enter the game, rather than the first. Well, so far in 2014, nothing changes, as the loss breakdown between starters and relievers remained unchanged. Even in prior years, just a handful of games were affected. So that tweak basically doesn’t matter. I’d still prefer to give it to the last tied pitcher, though, as it would match current practice if there were a tie for most loss points in a walk-off game.

One other side benefit of this little project: I’ve discovered a data error in my database, as total wins and losses don’t equal each other for 2011 and 2012. I’ll have to get that fixed!

Posted in Major League Baseball, Sabermetrics | 4 Comments

When the hurling Buehrle’s won… or perhaps why!

Blue Jays’ starter Mark Buehrle is off to a great start so far this season, leading the majors with 6 wins, with an ERA of 1.91. While not as impressive as his ERA, Buehrle’s FIP, fielding-independent pitching, is a still-excellent 3.04, which would be a career best by nearly half a run, and is more than a full run under his career mark of 4.10.

Buehrle has been a workhorse, noted more for his durability than anything else. The lefthander has made at least 30 starts, and pitched at least 200 innings, for 13 straight seasons. He has made 4 All-Star teams, and so far seems a good bet to make a fifth, but only once, in 2005, did he ever receive votes for the Cy Young Award. He’s been an innings eater, reliable, but not an ace. This year, however, he’s pitched like an ace.

So what’s behind his sudden improvement? Will he continue to dominate, or will owners who expect that face a tragedy of nearly Shakespearean proportions?

Continue reading

Posted in AL, Major League Baseball, Projections | Comments Off on When the hurling Buehrle’s won… or perhaps why!

Introducing RV Current

Today I’m introducing a new projections source, which I’m calling RV Current. The idea is to use the same basic projection model I use in the preseason, but also include current year data in the model. The goal is a current “true talent” estimate for each player.

So in addition to taking up to 3 years past data, I also add in current year’s stats. I give the current year weight based on the portion of the season we’ve played, such that after 81 games, I’d count this year’s stats as heavily as last year’s, and by the end of the season this year would count twice as much.

After doing the raw projections, I still make playing time adjustments based on depth charts and injury status. When a player is on an injury report with an estimated return timeframe, I’ll pick a date in the middle as a projected return date. When showing future statistics, I’ll use those dates to adjust projected playing time for players. The depth chart checks try to ensure starters get some minimum projected playing time, while bench players are capped (unless they’re on an injury report). Finally, I check a team’s projected totals to ensure they’re reasonable, both at the overall team level and at a per-position level.

Continue reading

Posted in Fantasy Strategy, Major League Baseball, Projections, RotoValue, Sabermetrics | Comments Off on Introducing RV Current

Hedging Your Bets on the Nets?

On Monday, Nate Silver looked at the odds of winning a second-round playoff series after going 7 games to win the first round, and found that your chances to win go down:

 Original SRS odds: Miami 88 percent to win the series.

Modified SRS odds: Miami 95 percent to win the series.

Miami swept its opening-round series, while Brooklyn needed seven games to beat the Toronto Raptors. Hence, the Nets have gone from really big underdogs to really, really big underdogs.

Today, FiveThirtyEight writer Benjamin Morris notes that the Nets beat the Heat in all four regular-season games between the two:

Brooklyn mainly has one thing going for it: The team swept its regular-season series against Miami, winning all four games.

Morris plugs this information into a model, and he summarizes his findings:

  • For teams of even strength, the home team is a 66 percent favorite if it was 4-0 against its opponent in the regular season, but a 45 percent underdog if it was 0-4.

  • The shift in odds for a two-game advantage (usually because of a 3-1 or 2-0 head-to-head record) is approximately the same as the value of having home-court advantage for the series(!).

  • While moderately impressive, this gets us nowhere close to making Brooklyn a favorite against Miami by virtue of its 4-0 record alone (though if this were all the information in the world, the 72 percent odds of Miami winning here would be much worse than the 85 percent predicted by the market).

I’ve been a Nets fan since they took University of Maryland teamates Buck Williams and Albert King together in the first round of the 1981 draft, so I hope Morris’s take carries more weight than his boss’s!

And initially, that seems to be the case: Silver estimated their chances of winning dropped about 7% because of going 7 games, while Morris’s finding about the regular-season sweep implies improving their chances by about twice that.

No matter who wins, one of these stories will make it look like these guys really know something!

Posted in NBA Basketball, Sabermetrics | Comments Off on Hedging Your Bets on the Nets?

Improving Future Statistics

While we can’t know the future, we can make educated guesses and projections about it. RotoValue does this by displaying projected stats (both my own projections, and for baseball, projections contributed from outside sources, Steamer, Marcel, and MORPS), and also by letting you choose prorated stats for the current year or the previous year.

When I first implemented prorated numbers, I simply divided a player’s current stats by the number of games his team had played, and then multiplied by the number of games in the season. If you were showing stats for less than the full season, I’d prorate to the number of games his team had scheduled over that time. That was a decent first-cut, but for players who missed much of the season so far, their prorated future numbers were too low. So, for example, Clayton Kershaw won his first start, pitching very well, but then went on the DL. Under my old model, since he pitched just one of the Dodgers’ 26 games so far, I’d prorate him to start just 1 out of every 26 games, or about 6 starts. His fantasy owners (not to mention the Dodgers) surely hope he pitches a lot more than that! Now, however, I’m tracking when players are actually on a team’s active roster, and using that information to better prorate statistics. Kershaw has made just 1 start, but since he was on the disabled list most of the time, I currently prorate him to make a total of 25 starts.

Simply prorating stats has another bias, though, one that also affects searches based on projected statistics. Now that the season is under way, players do get injured, and preseason projections did not reflect that information. Josh Hamilton tore a ligament in his thumb and is out for 6-8 weeks according to my injury reports. But the preseason projections for Hamilton don’t account for this new knowledge, but I now try to do that. I now compute a “target return” date for injured players, based on the data shown in the injury reports I receive. In Hamilton’s case, I’m adding 7 weeks to the April 9th date listed for his injury, and I set the target return to May 28th. So rather than showing Hamilton’s stats assuming he’ll play all the team’s remaining games, I prorate the projections as if he’ll only play from May 28th onward.HamiltonSearch
Here I’ve reduced the playing time for Hamilton, but kept his rate numbers the same as the original preseason Steamer projections:

This should better reflect the future value Hamilton might have to fantasy owners. So when you’re viewing projections in a Search page, or as part of a projected standings page, I’ll adjust projections based on a player’s target return from injury. The Player Detail page will continue to show the original projections as given to me by the source (or computed by me).

The target return date is used not only when showing projections, but also when showing prorated statistics. Where the injury report gives an estimate of the player’s return, I use that to get the target return date. If he’s on the disabled list, but without any other guidance, I use the first date he’s eligible to come off, unless that date is in the past, in which case I’ll arbitrarily say he’ll miss 10 more days. At this point I’m updating based on the injury reports themselves, and not other news stories about a player’s return. So while I’ve seen reports that Bryce Harper will be out until July, because the injury report currently just lists him on the 15-day DL, I’ve set his target return to May 11th, 15 days after he was put on the DL.

These enhancements are also used when I compute projected standings for a league, so those values should be improved overall.

One caveat, however: by ignoring time a player is not on the active roster for prorated stats, I do expose small sample size issues. Because he has made only one start so far, Kershaw prorates to have the 1.35 ERA and 0.900 WHIP he had in that one start, while Hamilton hit .444 in the 8 games he played before going on the DL. While the rate stats, are way overly optimistic, the cumulative totals are better than they would be if I simply assumed both players would only play a tiny fraction of the season, which is what the old prorating model did.

Numbers should never drive all your decisions in fantasy sports, but getting better numbers can better inform your decisions. And the projected and prorated statistics shown at RotoValue have just gotten better.

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Throwing your Weight Around

Much of the point of RotoValue dollar values is that the parameters of your league are relevant to how you should value players. When you start 2 catchers per team, top catchers are worth more than if you start just 1. Many players that have value in deeper leagues aren’t worth owning in shallower ones. My pricing model implicitly takes this into consideration.

But the site can customize even more than that. For a long time, the site has let RotoValue Analyst customers manually override the category weights, and also select extra categories to display for your league. Under the Settings menu, they’ll see a choice “RotoValue & Display”:

RotoValue Analyst customers can choose their own categories, and weights.

So if, for example, you’ve traded away all your closers, you could give the Saves category a weight of 0, and the RotoValue prices for your league would adjust to reflect the fact that you no longer care about a category. Similarly if you have a seemingly insurmountable lead in a category, you could give it 0 weight, or if a category is very close, you could give it double, or even triple weight. This page lets you set the weights for the categories in any RotoValue calculations, helping you find free agents (or evaluate trades) that are more relevant to your team’s situation. You get to manually set whatever weights you want, or, by clicking on the Reset button at the bottom of that page, remove all custom settings, and revert to using the scoring weights for your league in price calculations.

To use the customized weights, though, you have to opt in, by going to the settings page and updating them. I’ve just added a checkbox at the top of the page, “Compute weights based on your team’s stats and standings?”. This can automate the process of determining weights for you. If you select this option, the code will look at projected standings for your league, as well as the players in the active lineup on your team, and it will see how close you are to other owners in each category. The closer you are to passing or being passed by other owners, the more weight I will give a category. And if you save with this box checked, the algorithm runs very early each morning, after the previous day’s games, to recompute new weights based on updated standings data (and lineup changes).

Highlight auto-computed weightsIn this case my team projects near the bottom in Stolen Bases, and, more importantly, far from most other teams, so the algorithm gives that category the least weight, but WHIP projects to be the closest category, so it gives the most weight there. These different weights will affect RotoValue price calculations, guiding me towards players who are more likely to help my team, not only in general, but in the particular context of my league and opponents. The weights are scaled relative to the original league’s weight, so SB is given only 62% of its normal weight, while WHIP is given 134% of its weight. If your league weights certain categories differently for scoring purposes, then these calculations take that into account.

These auto-generated weights will tune RotoValue price calculations not only to the specifics of your league’s scoring and roster sizes, but also to the particular situation of your team and its current active roster relative to others. This information can help you make better decisions about pickups, trades, or even whom to put in your active lineup, and help you win your league.

Read on to see the how much difference this can have, and also details of the algorithm’s implementation. Continue reading

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