ROS Strength of Schedule: QBs,RBs, WRs, TEs, D/STs, &Ks

Nov 08

ROS Strength of Schedule: QBs,RBs, WRs, TEs, D/STs, &Ks

This post will look at the rest-of-season strength of schedule for all Fantasy Football positions. Last week we looked at the strength of schedule for the running back position, and it is time to finish off the work, pull out the spreadsheets, and come up with the calculations for the other five Fantasy Football...

Read More

Rest of Season Strength of Schedule for All 32 Teams: Running Backs

Oct 28

Rest of Season Strength of Schedule for All 32 Teams: Running Backs

For the purpose of player evaluation, trade candidates, and rest of season rankings, I thought I would look at the team rest-of-season strength of schedule. For this article in particular, we will focus on running backs, but I will post more articles in the near future about other positions. How did I do it? Easy: I took each team’s remaining opponents, and averaged their average Fantasy points allowed per week total to get an opponent average points allowed total for the rest of the season. The results (Note: these numbers do not include the totals from Week 8): RankTEAM91011121314151617Total Rank 1PHIOAKGBWSHBYEARIDETMINCHIDAL16.65 2GBCHIPHINYGMINDETATLDALPITCHI16.59 3DALMINNOBYENYGOAKCHIGBWSHPHI16.56 4TENSTLJACINDOAKINDDENARIJACHOU16.21 5ARIBYEHOUJACINDPHISTLTENSEASF16.13 6BALCLECINCHINYJPITMINDETNECIN15.92 7HOUINDARIOAKJACNEJACINDDENTEN15.91 8INDHOUSTLTENARITENCINHOUKCJAC15.90 9SEATBATLMINBYENOSFNYGARISTL15.80 10NYGBYEOAKGBDALWSHSDSEADETWSH15.78 11TBSEAMIAATLDETCARBUFSFSTLNO15.64 12SDWSHDENMIAKCCINNYGDENOAKKC15.59 13CLEBALBYECINPITJACNECHINYJPIT15.55 14CHIGBDETBALSTLMINDALCLEPHIGB15.36 15NEPITBYECARDENHOUCLEMIABALBUF15.34 16NYJNOBYEBUFBALMIAOAKCARCLEMIA15.30 17SFBYECARNOWSHSTLSEATBATLARI15.18 18BUFKCPITNYJBYEATLTBJACMIANE15.15 19DETBYECHIPITTBGBPHIBALNYGMIN14.99 20JACBYETENARIHOUCLEHOUBUFTENIND14.99 21MINDALWSHSEAGBCHIBALPHICINDET14.88 22CARATLSFNEMIATBNONYJNOATL14.82 23KCBUFBYEDENSDDENWSHOAKINDSD14.71 24PITNEBUFDETCLEBALMIACINGBCLE14.67 25STLTENINDBYECHISFARINOTBSEA14.65 26CINMIABALCLEBYESDINDPITMINBAL14.63 27ATLCARSEATBNOBUFGBWSHSFCAR14.61 28WSHSDMINPHISFNYGKCATLDALNYG14.60 29NONYJDALSFATLSEACARSTLCARTB14.44 30OAKPHINYGHOUTENDALNYJKCSDDEN14.29 31MIACINTBSDCARNYJPITNEBUFNYJ13.34 32DENBYESDKCNEKCTENSDHOUOAK13.18 Reactions First thought: Thank god I have some players toward the top of the list. Second thought: Not only do the coaches in Miami hate Lamar Miller, the schedule gods hate Lamar Miller as well. Third thought: I really need to get a new job; this entire article, and most of the other articles I write, are composed while I am at my real job. But hey, I guess most people don’t have jobs that would allow them to do this. Final thought: If you ever want to stir up controversy among friends during a night of drinking, serve up some “Screaming Nazis” (half Jagermeister and half Rumple Minze)—also known as “Blackout Juice.” Friend A and Friend B of mine got into a fight on Friday night/Saturday morning because Friend A told my roommate that he could eat faster than my roommate (this is all after Friend A, Friend B, my roommate, and I had been heavily lubricated with much more alcohol than the four Screaming Nazis that each of us split). Friend B, feeling the effects of the Screaming Nazi, and in objection to Friend A’s assertion, decked friend A in the face: Thwack! I, on the other hand, after drinking a Screaming Nazi, made a fool out of myself on the bus back from the bars in front of a girl whom I used to have a college class with; she looks like a hot...

Read More

Fantasy Basketball Projections: Top 69 Player by Basketball References Simple Projection System

Oct 23

Fantasy Basketball Projections: Top 69 Player by Basketball References Simple Projection System

In this post we will discuss Fantasy Basketball projections for the 2013-14 season. As I discussed in my last post about Fantasy Baseball ranking based off of the 2014 Steamer projections, I love rankings based off of quantitative analysis. Rankings that take a statistical approach to value ascription lack the histrionic overtones and biases that are present throughout most of the fantasy fodder online, and they provide market inefficiencies that owners can take advantage of when drafting. While there are very few quantitative NBA projection systems, compared to the amount of MLB projection systems, there are two that I found. Perhaps the most well known, Kevin Pelton’s SCHOENE projection system is available to ESPN Insiders, and I succumbed to the forces of the evil empire and subscribed to a 30 day free trial to procure the necessary information to write this article. Alas, ESPN Insider does not provide SCHOENE projections in an exportable format: BOOO! The second NBA Projection system that is widely available is the Basketball Reference Simple Projection System (SPS).  The SPS takes into consideration playing time, aging curves, and the last three years of player performance. Most importantly, the SPS has been proven to be accurate in its projections, and slightly more effective than the SCHOENE projection system. I took the SPS projections, translated them into fantasy value for a standard eight category rotisserie league—points, rebounds, assists, steals, blocks, three pointers, field goal percentage, and free throw percentage—and then weighted those projections with the total amount of minutes ESPN projects that player to play; the SPS weights all stats on a Per-36 minute performance rate, and you get weird results when you don’t take into consideration the amount of playing time a player will realistically be on the court. For example, Manu Ginobili projects to as a top twenty player when you don’t take into consideration the brief amount of minutes he plays. Here are the results of the Simple Projections System (Note these rankings do not have positional adjustments weighted into their projections, so all the centers need to get a huge boost): RkPlayer3PTRBASTSTLBLKPTSFG%FT%tMINwzSCR 1Kevin Durant0.920.400.520.500.513.681.714.46 3,080 20.35 2LeBron James0.150.482.021.360.023.382.940.50 2,865 16.17 3Chris Paul0.28-0.933.873.97-1.111.320.662.37 2,622 14.21 4James Harden1.44-0.651.151.36-0.622.640.153.45 2,902 13.46 5Stephen Curry2.73-0.931.961.36-0.952.190.401.89 2,865 12.89 6Kyrie...

Read More

2014 Fantasy Baseball Projections: Top 192 Hitters by Steamer

Oct 16

2014 Fantasy Baseball Projections: Top 192 Hitters by Steamer

The best way that I have found to come up with Fantasy Baseball projections is to take an existing statistical projection, and translate those numbers into fantasy value. Projections that use statistical models free players from fantasy analysts who have biases against players; “I’ll never own player X again after last year”; “He won’t be able to produce on that offense.” These are mistakes I see fantasy analysts make all of the time, and in the former assertion analysts make assumptions about a player’s future performance based off of a meager sample size, and in the latter situation analysts attack a player’s team, which matters some but not as much as some would like you to think. At the beginning of last year I took every statistical projection I could find (Zips, Steamer, Oliver, Marcel, FANS), averaged them together—statistical projections that aggregate a group of projections have proven to make forecasts on a particular topic 20% more accurate—and then translated those numbers into player rater value with the conventional means that rotisserie value is calculated. Last year the fruits of this labor produced Paul Goldschmidt and Adam Jones as top fifteen hitters when few other pundits had their pecking orders calibrated in such a way. Because the only projection system that I could get a hold of at the moment is the Steamer projection, these numbers should not be the end all be all, but it is still interesting to look at why these rankings are constructed the way they are? Said another way, what do these projections see in player X from a statistical prospective, that qualitative forecasters miss the boat on? Here are the Steamer batter rankings for the 2014 MLB season (These rankings are not adjusted for position, because I did not feel the desire to manually enter each player’s position into a spreadsheet for the next hour, but these rankings are still directionally correct): NameHRRRBISBAVGZSCR Miguel Cabrera3911312530.32912.65 Mike Trout2611579340.3059.85 Paul Goldschmidt33102105150.2888.60 David Ortiz329910620.2917.05 Jacoby Ellsbury149875410.2896.62 Andrew McCutchen238986220.3046.59 Adrian Beltre298710510.3056.46 Prince Fielder299810510.2896.26 Matt Holliday26959650.2986.09 Chris Davis398910140.275.95 Ryan Braun278386180.35.94 Robinson Cano26899840.3025.92 Troy Tulowitzki29859830.3045.81 Hanley Ramirez248589200.2835.31 Yasiel Puig257984220.2854.98 Joey Votto24928660.34.92 Adam Jones288195110.2814.88 Adrian Gonzalez26849910.2944.83 Torii Hunter21978650.2854.20 Michael Cuddyer23819090.294.13 Evan Longoria30879730.2684.09 Dustin Pedroia149281160.2924.08 Giancarlo Stanton36818940.2663.98 Anthony Rizzo30889370.2623.96 Shane Victorino158979250.2783.95...

Read More

A Pitcher’s True Strike Out Potential

Oct 04

A Pitcher’s True Strike Out Potential

Mike Podhorzer has looked into the relationship of a batters’ average fly ball distance as it relates to their HR/FB ratio, and has found results that will allow others to more accurately project a hitter’s home run totals from year to year. This got me thinking. Which can be a good or bad, but in this case, the authors’ labor produced a fruitful return. While a hitters’ HR/FB ratio can fluctuate indiscriminately from year to year, Podhorzer has proven a batters’ average fly ball distance is a better indication of a player’s true talent power production. In the same light, my study looks at how a player’s swinging strike rate (SwStr%) is a better indication of a pitcher’s strike out potential than K/9. My assumption was that K/9 and SwStr% have a strong relationship. But, how strong of a relationship is it? To find this out, I took all qualified starter seasons from 2003 to 2013, which gave me a sample size of 933 pitchers, and ran a correlation between their SwSTR% and their K/9. The results showed that there is an exceedingly positive correlation between SwSTR% and K/9, to the tune of a .807 correlation coefficient and a .65 R2. What is important to note is that there are very few pitchers present in the sample with a SwStr% above 13%, which may be symptomatic of something larger. Getting batters to swing and miss is difficult. The more often you can get a batter to swing and miss, the more valuable you are as a pitcher. As a result, the higher the SwStr%, the smaller the sample size becomes. For example, Johan Santana (2004) and Kerry Wood (2003) are the two lone dots to the farthest right on the graph with SwStr% of over 15: wow. After the relationship between SwStr% and K/9 ratio became unmistakable, I calculated what a particular SwSTR%s translates into, as far as K/9, with the formula Y=68.473*x+0.8435, and got this chart: The next step is to take what we have discovered and apply it to a sample. The chart below shows each qualified pitcher for 2013, their SwStr%, xK/9, K/9, and K/9-xK/9.  xK/9 is what we would expect a pitcher’s K/9 to be...

Read More

Best Streaming Options: Five Options to Win Your League!

Sep 26

Best Streaming Options: Five Options to Win Your League!

Since the major league season is over on Sunday, that means the end of fantasy baseball, and the end of this source for the best streaming options. I will write a wrap-up column that will be posted next monday that will summarize my performance over the last two weeks. But, there will also be many columns to come during the offseason, and if you want to be notified of when those columns will go up, follow me at @devonjjordan. f you read any of the other articles in the streaming series, you can skip the first section of this post and start to read where I go over the best streaming options. To make your options realistic and reach a wider range of players, we will keep the pool of players eligible to be streamed to those pitchers that are owned in 50% or less of ESPN leagues.  (If you don’t know what wRC+ is, this is a link to the FanGraphs library definition) Here are the best streaming options for Thursday through Sunday: Thursday: Zach McAllister vs. Minnesota (At Minnesota) IF YOU ARE DESPERATE ESPN Ownership: 11.6% Opponent’s wRC+: 92 vs. RHPs (20th in MLB) If you have to find someone available to start on Thursday, Zach McAllister is your best option, but here are some reasons not to start Zach McAllister, and make you rethink whether or not you actually need to stream a pitcher on Thursday.  McAllister is in the top 20 twenty for starting pitchers in fly ball percentage. Which is not necessarily bad when you pitch at Progressive field for your home games, which has been in the bottom third of ballparks for runs allowed the last two years, but it is a problem when you play a Target Field, which has been 10th and 12th in runs allowed the last two years, and happens to be where McAllister will play the Twins on Thursday. Friday: Rick Porcello vs. Miami (At Miami) ESPN Ownership: 29.9% Opponent’s wRC+: 68 vs. RHPs (30th in MLB) For the same reason that you do not want to start Zach McAllister—the park factors at Target Field—you want to start Rick Porcello at Miami on Friday. Marlins Park was 26th in 2012 and...

Read More

Best Streaming Options: Four Starters That Could Win Your League

Sep 23

Best Streaming Options: Four Starters That Could Win Your League

If you read any of the other articles in the streaming series, you can skip the first section of this post and start to read where I go over the best streaming options. From now till the end of the season, I will go over the best streaming options available for the upcoming week; Monday’s post will have the best options for Monday through Wednesday, and Thursday’s post will have the best options for Thursday through Sunday. To make your options realistic and reach a wider range of players, we will keep the pool of players eligible to be streamed to those pitchers that are owned in 50% or less of ESPN leagues.  (If you don’t know what wRC+ is, this is a link to the FanGraphs library definition) Here are the best streaming options for Monday through Wednesday: Monday: Jose Quintana vs. Toronto (In Chicago) ESPN Ownership: 11.3% Opponent’s wRC+: 84 (27th in MLB)  The Blue Jays lineup that was a shell of a formidable unit against LHPs this year has lost most, if not all, of the firepower that made them competitive against southpaws. Jose Bautista, Edwin Encarnacion, and Melky Cabrera have all been lost to season ending injuries, and without the core of the lineup, the Blue Jays have posted a 97 wRC+ in September. Yordano Ventura vs. Seattle (At Seattle) ESPN Ownership:  0.7% Opponent’s wRC+: 98 (15th in MLB) While Ventura showed the ability to throw three pitches against the Indians in his major league debut, it is clear that the young flame flower’s most valuable pitch is his fastball. Ventura averaged 97.2 MPH on his fastball, while he also plateaued at 101.9 MPH. For his second career start, Ventura will go against the Mariners who are average against RHPs, and love to swing the bat: 21.8 K% vs. RHPs (5th in MLB). Tuesday: Tyson Ross vs. Arizona (In San Diego) ESPN Ownership: 10% Opponent’s wRC+: 96 (16th in MLB) Why has Tyson Ross so been effective this year? He’s striking out more batters than he ever has before; he’s gone from 5.65 K/9 in 2012 to 8.59 K/9 this year. How has he been striking more batters out? More fastballs, and more sliders. Ross threw a two-seam fastball on...

Read More
Page 4 of 6« First...23456
Follow

Get every new post delivered to your Inbox

Join other followers: