Brandon Phillips was a great baserunner this past season. He stole 23 bases and was only caught stealing three times. It wasn’t an all-time great season in terms of stolen bases or baserunning runs overall, and his baserunning is overshadowed by the baserunning greatness of teammate Billy Hamilton, but we can all agree that Phillips put together a very nice season on the basepaths.

Now let’s make things interesting. In contrast to his great 2015, Brandon Phillips was very bad at stealing bases the last few years. In 2013 and 2014 he combined for a grand total of seven stolen bases and six times caught stealing (Phillips in fact had negative net stolen bases in 2014, being caught stealing three times and stealing just two bases), being worth negative runs on the basepaths both years. We now have a rare situation on our hands, where a player was a prolific base-stealer after doing nothing the year before.

Let’s quantify Phillips’ improvement to find some historical comparisons. Here’s the complete list of players that increased their stolen-base total by at least 20 a year after having negative net stolen bases (stolen bases -t imes caught stealing):

I know it can be difficult to read through that entire list, so let me summarize it for you: Before Brandon Phillips in 2015, no player had ever, following a season with negative net stolen bases, increased their stolen-base total by over 20 in the following season!

Pretty cool, right? It gets even better!

Here’s what makes Brandon Phillips’ 2015 season on the basepaths even more unique. Brandon Phillips was also very old this season, turning 34 in the middle of the summer. While it’s not unheard of for old guys to steal lots of bases (Lou Brock stole 118 at 35), it is a lot rarer than players in their primes stealing lots of bases. What is very rare is for old guys to suddenly make a leap in their stolen-base totals.

Let’s go back to the numbers again to find some historical comparisons. Here is the complete list of players who had a 20-stolen-base increase at Brandon Phillips’ age or older since baseball became integrated:

Only five other players since integration have had a 20-stolen-base jump at Brandon Phillips’ age or older. And these aren’t any random players — with Brock, Henderson, Wills, and Campaneris on the list, you have the 1st, 2nd, 14th, and 20th career leaders in stolen bases. The 5th is Jose Canseco, which just confirms what we already knew: Jose Canseco is weird. Canseco’s performance late in his career was also famously PED-boosted to defy normal aging curves, but I decided to just present the stats to you and you could make your own judgment on which performances you consider legitimate.

Even compared to the four all-time great base-thieves and Canseco, Phillips’ 2015 season is still unique. Since integration, Brandon Phillips is the only player his age to ever have an increase of 21 in stolen bases while matching his success rate!

If you had predicted before the season that Brandon Phillips would steal less than 23 bases, no one would have doubted you. After all, 18,845 players have played major-league baseball before and not a single one had accomplished what Brandon Phillips needed to do.

However, as the saying goes, baseball is played on the field and not on a computer. Against all odds there was old Brandon Phillips, chugging along on the basepaths and making his mark in history while doing it.

Notes:

(1) I used a cutoff of 200 at-bats in each consecutive season for players to qualify for the stolen-base-increase list. This was because I wanted the increases in stolen bases to be due to the player’s actions, and not just more playing time. A season where a rookie is called up and steals two bases in five games, and then steals 50 bases in a full season the next year is obviously against the spirit of seeing which players increased their stolen bases the most. I generously made the cutoff to qualify very low to include as many players as possible and so I couldn’t be accused of cherrypicking an at-bat limit to help Brandon Phillips stand out.

(2) A lot of players in the 1890s and 1900s qualified for the 20+ stolen-base increase at 34 years old or later, but since the game was so different back then I decided to just compare Phillips against players from the modern era.

(3) Dave Roberts came close to making the second cutoff, but was just a bit younger than Brandon Phillips.

Part 2 of a series of posts regarding a new statistic, xHR%, and its obvious resultant, xHR, this article will examine formula 1. The primer, Part 1, was published March 4.

As a reminder, I have conceptualized a new statistic, xHR%, from which xHR (expected home runs) can be derived. Furthermore, xHR% is a descriptive statistic, meaning that it calculates what should have happened in a given season rather than what will happen or what actually happened. In searching for the best formula possible, I came up with three different variations, all pictured below with explanations.

HRD – Average Home Run Distance. The given player’s HRD is calculated with ESPN’s home run tracker.

AHRDH – Average Home Run Distance Home. Using only Y1 data, this is the average distance of all home runs hit at the player’s home stadium.

AHRDL – Average Home Run Distance League. Using only Y1 data, this is the average distance of all home runs hit in both the National League and the American League.

Y3HR – The amount of home runs hit by the player in the oldest of the three years in the sample. Y2HR and Y1HR follow the same idea. In cases where there isn’t available major league data, then regressed minor league numbers will be used. If that data doesn’t exist either, then I will be very irritated and proceed to use translated scouting grades.

PA – Plate appearances

(Apologies for my rather long-winded reminder, but if you really forgot everything from Part 1, then you should really invest in some Vitamin E supplements and/or reread the first post.)

The focus formula of this post is the first one, which also happens to be the one I think will work the least well because it relies too heavily on prior seasons to provide an accurate and precise estimate of what should have happened in a given season.

In the second piece of the formula, with only fifty percent of the results from the season being studied taken into account, it likely fails to take into account the fact that breakouts occur with regularity. As a result, it probably predicts stagnation rather than progress.

Methodology

Luckily for myself and the readers, the process was an incredibly simple one. Pulling data from FanGraphs player pages, ESPN’s Home Run Tracker, and various Google searches, I compiled a data set from which to proceed. From FanGraphs, I collected all information for Part Two of the formula, including plate appearances and home runs. Unfortunately, because a few of the players from the sample were rookies or had fewer than three years of major league experience, I had to use regressed minor league numbers. In some cases, where that data wasn’t applicable, I dug through old scouting reports to find translatable game power numbers based off of scouting grades (and used a denominator of 600 plate appearances).

Then, from ESPN’s amazingly in-depth Home Run Tracker website, I obtained all relevant data for player home run distance, average home run distance for the player at home, and league average home run distance. Due to my limited time, I only used players that qualified for the batting title during the 2015 season, yielding an iffy sample of only 130 players. Additionally, before anyone complains, please realize that the purpose of my research at this point is only to obtain the most viable formula and refine it from there.

Results

Using Microsoft Excel, I calculated the resultant xHR% and xHR. Some key data points:

League Average HR% (actual):  3.03%

Average xHR%:  2.85%

Average Home Runs: 18.7

Expected Home Runs: 17.7

Please note that there is a significant amount of survivorship bias in this data. That is, because all of these players played enough to qualify for the batting title, they are likely significantly better than replacement level, which is why the percentages and home runs seem so high.

Clearly, the numbers match up fairly well, with this version of the formula expecting that the league should have hit home runs at a .18% lower clip, and one fewer per player, which amounts to a significant difference. Over the course of a 600 plate appearance season, the difference between them is still only a little more than one home run, an acceptable distance.

Correlation between xHR% and HR%: 0.960506092

R² for above: 0.922571953

HR% Standard Deviation: 1.5769373

xHR% Standard Deviation: 1.3883746

Correlation between xHR and HR: 0.966224253

R² for above: 0.933589307

HR Standard Deviation:  10.43771886

xHR Standard Deviation: 9.201355342

While xHR% using this formula apparently explains about 92% of the variance, correlation may not be the best method of determining whether or not the formula works adequately. This holds at least for between xHR% and HR%, because there’s only a minuscule difference between their numbers (but one that matters), meaning it’s not a particularly explanatory method and that it may not have the descriptive power I’m looking for. Nevertheless, it is important to note that the correlation is not a product of random sampling, as p<.005. Unsurprisingly, the standard deviation for xHR% is smaller than that of HR% (nearly insignificantly so), indicating that the data is clumped together close to the mean as a result of using this formula, a potentially good thing (in terms of regression).

A better indicator of the success of the formula is the correlation between xHR and HR, a relatively high value of ≈.97. Here, presumably because the separation between home runs and expected home runs is greater, the formula ostensibly explains approximately 94% of the variance in outcomes and resultant data. However, in this case, the standard deviation for actual home runs is about 10.4, while for xHR it’s about 9.2, suggesting that, after being multiplied out by plate appearances, xHR is spaced nearly as evenly as HR. Ergo, it likely serves as a decent predictor of actual home runs.

Players of Interest

Mr. Bryce Harper – It’s likely there isn’t a better candidate for regression according to this formula than Bryce Harper, who the formula says have hit only 32 home runs as opposed to his actual total of 42. While he did lead his league in “Just Enough” home runs with 15, he’s also always been known for having prodigious power (or at least a potential for it). Furthermore, Mr. Harper dramatically changed his peripherals last season to ones more conducive to power. Suggesting this are the facts that he increased his pull percentage from 38.9% to 45.4%, his hard hit percentage from 32% to 40%, and his fly ball percentage from 34.6% to 39.3%. On their own, all of the previous statistics lend credence to the idea that Harper changed his profile to a more home-run-drive one, but when taken together they significantly suggest that. His season was no fluke, and the formula certainly failed him here because it weighted prior seasons far too heavily.

Mr. Brian Dozier – No surprises here. Mr. Dozier has certainly been trending upward for a long time, and in a model that heavily weights prior performance such as this one, upticks in performance are punished. Nevertheless, the data vaguely supports the idea that Dozier should have hit 24 home runs instead of 28. While he did significantly increase his pull percentage to an incredibly high 60% from 53%, he did play in a stadium where it’s of an average difficult to hit pull home runs as a right-handed hitter. Moreover, 10 of his 28 home runs were rated as “Just Enough” home runs, in addition to his average home-run distance being 12 feet below average (admittedly not a huge number, nor a perfect way of measuring power). If I were a betting man, I’d expect him to hit 4-6 fewer home runs this coming season.

Keep watch for Part 3 in the coming days, which will detail the results of the other formulas. Something to watch for in this series is the issue that the results of the formula correspond too closely to what actually happened, which would render it useless as a formula.

Note that because I have never formally taken a statistics course, I am prone to errors in my conclusions. Please point out any such errors and make suggestions as you see fit.

One of the most important developments in statistics — and its subordinate field, sabermetrics — is the usage of multiyear data to produce an expected outcome in a given year. It’s an old concept, one that’s been around for centuries, but it likely originated in sabermetrics circles with Bill James. In Win Shares (arguably the birth of WAR), the sabermetric response to Principia Mathematica, he details a procedure of finding park factors wherein the calculator uses a weighted average of several years of data in conjunction with league averages to find park factors for a certain ballpark.

Methods such as Mr. James’s allow the amateur sabermetrician (and even the mighty professional statistician) to determine what ought to have happened over a specific time period. Essentially, a descriptive statistic. The best example of a descriptive statistic for the unlearned reader is xFIP, which basically describes what a pitcher’s fielding-independent average runs allowed would have been if the pitcher had a league-average home runs per fly ball rate.

Several statistics fluctuate greatly from year to year and are thus considered unstable. Examples include BABIP, HR/FB% for pitchers, and line-drive percentage. HR/FB% in particular is very fluid because all sorts of variables go into whether a ball leaves the park or not. For instance, on a particularly windy day, an otherwise certain dinger might end up in the glove of an expectant center fielder on the warning track instead of in the beer glass of your paunchy friend in the cheap seats. Rendered down, xFIP takes the uncontrollable out of a pitcher’s runs-allowed average.

With this, and an excellent article about xLOB% from The Hardball Times, in mind, I started developing my own statistic a few days ago. xHR%, as I dubbed it, attempts to find an expected home-run percentage, and from there one can easily find expected home runs (xHR) by multiplying xHR% by plate appearances, a more understandable idea to the casual baseball fan. In order to calculate this, I wrote several different (albeit very similar) formulas:

More likely than not, your eyes glazed over in that section, so I will explain.

HRD – Average Home Run Distance. The given player’s HRD is calculated with ESPN’s Home Run Tracker.

AHRDH – Average Home Run Distance Home. Using only Y1 data, this is the average distance of all home runs hit at the player’s home stadium.

AHRDL – Average Home Run Distance League. Using only Y1 data, this is the average distance of all home runs hit in both the National League and the American League.

Y3HR – The amount of home runs hit by the player in the oldest of the three years in the sample. Y2HR and Y1HR follow the same idea. In cases where there isn’t available major-league data, then regressed minor-league numbers will be used. If that data doesn’t exist either, then I will be very irritated and proceed to use translated scouting grades.

PA – Plate appearances

(For the uninitiated, HR% is HR/PA)

Essentially, what I have created is a formula that describes home-run percentage. First off, I used (.5)(AHRDH) + (.5)(AHRDL) in the denominator of the first part because a player spends half his time at home and half on the road. If I were so inclined, I could factor in every single stadium that gets visited, weight the average of them, and make that the denominator, but that’s just doing way too much work for a negligible (but likely more accurate) effect. Besides, writing that out in a formula would be a disaster because then there essentially couldn’t be a formula. Furthermore, having half of the denominator come from the player’s home stadium factors in whether or not the stadium is a home-run suppressor or inducer, which helps paint a more accurate picture of the player.

Dividing the player’s average HRD by(.5)(AHRDH) + (.5)(AHRDL) allows the calculator to get a good idea of whether or not the player was “lucky” in his home runs. If his average home-run distance is less than the average of the league and his home stadium, then it follows that he is a below-average home-run hitter and his home-run totals ought to be lesser.

Since the values in the numerator and the denominator will invariably end up close in value to each other, I decided that this part of the formula could be used as the coefficient (as opposed to just throwing it out) because it will change the end number only slightly. Moreover, the xCo (as I call it) acts as a rough substitute for batted-ball distance and park dimensions in order to factor those into the formula.

The second part, the meat of the formula, uses a weighted average of multiple years of home-run-percentage data to help determine what should have been the home-run percentage in year one (the year being studied). Basically, it helps to throw out any extreme outlier seasons and regress them back a little bit to prior performance without stripping out everything that happened in that season (notice that in every formula the biggest weight is given to the season studied).

At this juncture, I cannot say for certain how much weight ought to be given to prior seasons. Obviously, a player can have a meaningful and lasting breakout season, with continued success for the rest of his career, making it inaccurate to heavily weight irrelevant data from a season two years ago. On the other hand, a player can have a false breakout, making it better to include more data from previous seasons. Undoubtedly that will be the subject of future posts. At present, the formula is a developmental one that will no doubt experience heavy changes in the future.

For the interested reader, some prior iterations of the formula are below:

As a reminder, with some small addenda, here is the explanation for each variable:

HRDY3 – Average Home Run Distance Year Three (year three being the oldest of the three years in the sample). HRD is calculated with ESPN’s home run tracker. HRDY2 and HRDY1 follow the same idea.

AHRDH – Average Home Run Distance Home. Using only Y1 data, this is the average distance of all home runs hit at the player’s home stadium by any player.

AHRDL – Average Home Run Distance League. Using only Y1 data, this is the average distance of all home runs hit in both the National League and the American League.

Y3HR – The amount of home runs hit by the player in the oldest of the three years in the sample. Y2HR and Y1HR follow the same idea. n cases where there isn’t available major league data, then regressed minor league numbers will be used. If that data doesn’t exist either, then I will be very irritated and proceed to use translated scouting grades.

PA – Plate appearances

(You should be initiated at this point, so figure out HR% for yourself.)

The reason these formulas were thrown out was that the xCo relied too heavily on seasons past to provide an accurate estimate. When I briefly tested this one on a few players, it delivered incredibly scattered results. Furthermore, there wouldn’t be any data available for rookies to use these iterations on because there’s no such thing as a minor-league or high-school home-run tracker (and if there were I probably wouldn’t trust it). The first formulas described are overall more elegant and more accurate.

Stay tuned for Part 2, when results will be delivered instead of postulations.

Typically, projections and conjecture about the upcoming baseball season serve the general purpose of piquing your interest. However, sometimes they are good for making money. In this instance, here are some gambles you can make based on the Atlantis Race and Sports Book. 

This article was written on February 28, 2016 and the initial lines from this Fox Sports article were published on February 12, 2016.

The team win projections referenced are some basic (keyword, “basic”) projections I made for this season. 

1. Colorado Rockies — Over 68 1/2 Wins, -110

• Projected Wins: 81

The projection for the Rockies is shockingly bullish at first glance. But, take a step back and put it in context. The Rockies gave up 844 runs last year, the highest amount in MLB. This year they are projected to surrender 757, or 87 less runs; an improvement of over a half-run per game.

This is not ridiculous considering what you can expect from their pitching staff. They will have a full season from a maturing Jon Gray and they bolstered their bullpen with Jason Motte, Chad Qualls, and Jake McGee. These highlights may not be awe-inspiring, but they don’t need to be. The 757 projected runs against is the worst projected runs against in the NL. The projection doesn’t signify the Rockies are good; they signify they are not as bad as last year.

The Rockies offense is projected to keep chugging along, with 761 runs scored, which would be the ninth-lowest runs scored for a Rockies team from 1995–2015, and only 24 runs greater than last year’s Rockies team. It’s not all that extreme.

You don’t need to buy into the projections to view this as a good bet. You just need to buy into the idea that the Rockies are better than they were last year (when they won 68 games). The Rockies are the best bet at the dawn of spring training.

2. Chicago Cubs — Over 89, -110

• Projected Wins: 100

A pessimist may ask some of the following questions of the Cubs: (1) It’s the Cubs. Will they find some way to blow it?; (2) Will Jake Arrieta be able to carry over his performance of the past season and a half?; (3) Will Kris Bryant and Kyle Schwarber suffer a decline in performance now the league has had an off-season to study their strengths and weaknesses?

A pessimist would probably have more questions along these lines, but a pessimist would have more of these types of questions about other teams. So, don’t be a pessimist; play the odds, particularly if you’re betting. The odds say the Cubs are the best team in the league.

You may not want to bet on the Cubs’ projected win figure of 100, but it seems foolish to not bet on 90+ wins. Teams can be ravaged by injuries (see 2015 Washington Nationals) and teams can be ravaged by bad luck, but don’t let the world of possibilities cloud the virtue of probabilities. The probability that the Cubs win over 90 games for the second year in a row is greater than the pessimistic possibilities that may (but probably aren’t) dancing through your head.

3. Los Angeles Dodgers — Over 87, -115

• Projected Wins: 95

How much can one man be vilified? Snark surrounded Andrew Friedman and the Dodgers’ offseason, beginning with the departure of Zack Greinke. It continued as the Dodgers added more starting pitchers to their pitching staff than they did former general mangers to their front office staff. But that’s okay. You know better, don’t you?

This writer is hard-pressed to think of a team so well-equipped to survive the maladies and booby traps that a major-league-baseball team may encounter in a trek through a 162-game season (well, all but Clayton Kershaw’s arm falling off). They have a cadre of infielders (Kendrick, Turner, Utley, Seager, Guerrero), outfielders (Puig, Pederson, Ethier, Crawford, Van Slyke, Thompson), and Enrique Hernandez is essentially baseball’s equivalent to the utility knife. As suggested in the first paragraph, the Dodgers’ positional depth may only blush when it encounters the depth of their own pitching staff.

If you doubt the Dodgers, you may be the kind of person who’d choose a wallet with a $100 bill over another with ten $20 bills. But, don’t fear if you did that, you can turn that $100 into $187 if you bet on the Dodgers to win more than 87 games this year.

If you’re still unsure, you should have chose the wallet with ten $20 bills. You wouldn’t need to gamble at all if you did that.

4. Washington Nationals — Over 87, -115

• Projected Wins: 94

I will not blame you if you begin to feel a greater degree of uncertainty at this point. The luster may have come off the Nationals last year, but don’t you believe they could be re-polished? It’s feasible the Mets and Nationals (and maybe the Marlins) take the battleground of the mid-80s to determine the NL East champion, but it’s more likely that the division winner will walk away with more than 90 wins, or the Nationals will surpass everyone at that level.

You may not want to bet on the health of Stephen Strasburg, Anthony Rendon, and Jayson Werth. Or, you may just want to bet. If the latter is the case, the Nationals are a good bet; not a sure bet. But what is a sure bet? The Nationals’ biggest offseason splash was Daniel Murphy, but their most effective offseason acquisitions likely went under the radar. They bolstered their bullpen with the additions of Shawn Kelley, Oliver Perez, Yusmeiro Petit, and Trevor Gott. They also have a farm system that can (1) patch holes this year (Lucas Giolito) and (2) be used to acquired talent to fill any other holes through trade.

Oh, and Dusty Baker is their manager. You can feel how you want about that, but that means Matt Williams isn’t their manager this year and there’s only one way to feel about that.

5. Kansas City Royals — Under 87, -115

• Projected Wins: 74

Lets establish two things: (1) The projected wins are low, and (2) the universe may haunt you for making this bet.

Disregard the universe for the moment. The Royals should be the favorites to win the AL Central. I don’t state that in a hypothetical way. There is no team in the AL Central that is so good that you should expect them to overcome the Royals’ Black Magic. But, for purposes of this exercise, ask the important question: Is the Royals’ Black Magic so good that it will propel them to win more than 87 games? I think not.

Much like the Nationals, I wouldn’t take my last $115 and make this bet, but if you want to bet on, say, five over/under win totals for a MLB team, I would make this your fifth bet. But realize, you’re not making a bet on a the performance of a baseball team; you’re making a bet on the rhythms of the universe.

If you’re hesitant to bet on the universe, here are some other reasonable (but not as reliable) choices:

6. Boston Red Sox — Over 85 1/2 Wins, -105

• Projected Wins: 89 

7. Toronto Blue Jays — Under 87 Wins, -110

• Projected Wins: 82

8. Texas Rangers — Under 86 Wins, -110

• Projected Wins: 81

9. Detroit Tigers — Under 85 Wins, -115

• Projected Wins: 79

10. Baltimore Orioles — Under 80 1/2 Wins, -110

• Projected Wins: 75

Land and water tend to do two different things when it comes to heat – the land retains it, while water repels it. The land’s retention of heat gives way by the afternoon, causing the rising heat to create a vacuum, which sucks in cooler air sitting on the surface of the ocean. Cool air rushes into the coasts by mid to late afternoon.

Petco Park is less than one mile from the Pacific Ocean, making it susceptible to these afternoon sea-breeze gusts, which tend to pick up in the spring time and fade in the summer. Fortunately, the ballpark is situated east of Coronado Island [1], which helps to buffer the would-be stronger sea breezes that might affect fly balls. The spring time gusts, the Coronado Island buffer, and the “effect” on fly balls are all hearsay. We’ll look closer at each of these, starting with the sea breezes at the ballpark.

The Wind Matters

Let’s take a closer look at how the wind affects fly balls at Petco Park. Not that the common word of the good people of San Diego can’t be trusted; it’s just a matter of science. Below is a graph of every home run hit at Petco Park over the last two years and the approximate wind speed while the home run was hit. It seems like there’s no correlation between wind speed and distance of home runs.

However, not all wind is created equal, so the directional changes of the wind might have some influence on the flight of the ball. In the 2014 and 2015 seasons, the directional path of the wind for 261 home runs was registered (the wind was either “calm”, “variable”, or “NNE” which registered in only one case).

Most home runs were hit while the wind was blowing in the west-northwesterly (WNW) direction. Given that center field is due north of home plate that would mean that a majority of wind is probably blowing over the Western Metal Supply Co. brick building. My guess (I’m not a meteorologist) is that the wind is drawn in from the ocean, over the top of Coronado Island. Here’s a bird’s eye view of Petco; the arrow indicates where the wind is coming from – it’s the WNW direction from home plate.

So, this begs the question: How does WNW wind affect the distance of home runs? If we only look at the 101 home runs hit while the wind was blowing from the WNW direction, we begin to see something going on (r = – .21, p = .04. For every 1.53 mph faster the wind blows from the WNW direction, 1 foot is lost from every home run hit (R2 = .04, p = .04, n = 101). 

No other individual direction of wind registered a significant influence of the distance of home runs hit, nor did the combination of every other wind direction have any effect. So much for the Coronado Island buffer.

It’s a decent speculation that the direction in which a home run was hit (left, right, center) might be more or less affected by the WNW wind. However, the direction that the home run was hit had no effect on the relationship of the distance of the home run, with respect to the speed of the wind. Exit velocity (the speed of the ball off the hitter’s bat) is an obvious predictor of home run distance. Exit velocity did show the weakest correlation with home run distance when hit in the WNW direction as compared to every other direction [2]. It’s likely that lower exit velocity means that the home run hit spent more time spent in flight, and was thus more susceptible to WNW winds that suppressed its total distance, regardless of the direction that it was hit.

Addressing the hearsay

Wind direction and wind speed were recorded ten minutes before every hour of every home game for the last two seasons [3,4]. No surprise, WNW winds dominate during the course of every home game.

Wind speed does seem to be higher in the afternoon a compared to the evening, peaking in the late afternoon.

Additionally, May tends to have the strongest winds, but July and August have produced stronger winds than April. The theory that the spring is windier than the summer isn’t entirely true, but the spring does contain the windiest month of the regular season (May).

Why does this research matter?

Obviously, the pitcher and the batter are going to matter most. But, the WNW wind explains about 4% – 5% of the reason why the home run ended up where it did (R2 = .044). If you’re the Padres and you play 81 home games a year 4% – 5% might mean something to you [5].

Here’s a crazy idea: let’s say you’re the Padres and you’re playing an afternoon (3pm – 5pm) game and the winds are blowing in from the WNW (there are at least 22 home games this 2016 season that will be played between 3pm and 5pm). If it’s early in the game, start Carlos Villanueva, who has a career 40.4% FB%, and if it’s later in the game, use Jon Edwards who had a 67.6% FB% in 52 innings between AAA and majors last season. Meanwhile, give Matt Kemp a break (who has a career 36% FB%) and platoon rookie Travis Jankowski who showed a 27% FB% in 34 games last year with the Padres.

Caveats

Why did I only choose the last two years? Wind patterns and sea breezes can change over time [6]. If we rewind the years, we may or may not see similar results. I felt that the last two years were a decent idea about what we could expect from 2016, any further back, and I might have run into a different profile. Don’t agree with these results? Add a few years, and let’s see if the trend holds — I’m all for more objectivity.

Yes, sea breezes could entail the “marine layer” which brings a body of cool and moist air into the ballpark, and I might take a look at that with my next article. However, it’s not the moisture that will suppress home runs — it’s the cool air. Warm air expands and lowers the air density, which results in less resistance on the baseball. Therefore the cooler the air is, the higher the density. Water (H2O) is less dense than atmospheric O2 and N2, therefore if there’s more moisture in the air, we’d see less resistance on the baseball [1]. Temperature, dew point, humidity, and pressure had no effect on the distance of home runs between 2014 and 2015.

[1] http://www.sandiegouniontribune.com/news/2011/jun/01/marine-layer-formidable–faraway-fences/

[2] Of the 4 directions that reported significant effects: North Northwest (r = .674, p < .01, n = 16), Northwest (r = .473, p < .01, n = 45), West Northwest (r = .393, p < .01, n = 101), West (r = .591, p < .01, n = 36)

[3] http://www.weatherforyou.com/reports/index.php?forecast=pass&pass=archive&zipcode=&pands=petco+park%2Ccalifornia&place=petco+park&state=ca&icao=KSAN&country=us&month=04&day=28&year=2015&dosubmit=Go

[4] https://www.wunderground.com/history/airport/KSAN/2016/02/23/DailyHistory.html?req_city=San%20Diego&req_state=CA&reqdb.zip=92101&reqdb.magic=1&reqdb.wmo=99999

[5] Quality of batter and/or pitcher was not tested in a multiple regression model, nor were any other predictor variables beyond wind speed. 

[6] See Coors Field effect: http://m.mlb.com/news/article/45755012/with-subtle-changes-to-dimensions-padres-hope-petco-park-plays-fair

Some know him as “The Freak”, while others like myself know him as “Big Time Timmy Jim“. Tim Lincecum is planning on showing if he’s got anything left in the tank sometime next month. This year he had some problems with his hip and ended up getting surgery in mid-September. Here’s a link to a some info about hip labrum surgery for those who are interested. Early in his career he was one of the most dominant starters out there and you could make an argument that for a short period he was the most dominant pitcher in baseball. Over the last four years he’s become a dependable 4th or 5th starter, but the 2015 season was one of the worst of his career.

Age has seemingly caught up with another pitcher. Lincecum is yet another example of a pitcher whose velocity peaked early in his career and has been on a decline ever since. We don’t have PITCHf/x data for his rookie 2007 season, but we have the data for the rest of his career. Besides the 2011 season where he regained some form, he’s shown a pretty consistent decline in velocity over time.

To me, the obvious outlier is the most recent season where he saw his average fastball velocity dip below 88 MPH and about 2 MPH slower than the 2014 season. This is where we can see how his hip issues affected his velocity on the mound. Below is table with his peripheral stats (excluding his rookie season). To give a quick overview, K/9 has been trending downward, possibly relating to his diminished velocity. It doesn’t look like his BB/9 or HR/9 has any significant trend, but FIP has almost always been more generous than ERA.

As I said before, Lincecum recently had hip surgery and I assume he is nearing the end of his rehab since he’s planning a February showcase to try and secure another contract. Given his uncertain injury status, and his performance over the last four years, he’s likely only going to be able to secure a 1-year contract possibly with some performance bonuses. Teams are definitely taking a risk if they decide to sign him, since over the last two years he has been just slightly above replacement level, accumulating o.1 WAR in 2014 and 0.3 WAR in 2015. I’ll also mention that as a starter in 2014 he was worth 0.3 WAR, and he was worth -0.2 WAR as a reliever.

He’s certainly not the most imposing pitcher to ever set foot on the mound, standing 5′ 11″ and weighing in at 170 lbs (maybe with a wet towel wrapped around his waist); he’s one of those pitchers who needs to use his whole body to gain the necessary momentum to get those 90+ MPH fastballs. If you go back and look at the fastball velocity chart above it’s pretty clear that there was a significant drop in velocity this previous season. I think it’s pretty fair to think that his hip issues had something to do with that phenomenon. Here’s a link to an article from MLB Trade Rumors with some info about his surgery. I remember reading a more in-depth article earlier in the off-season saying that his hip issues were screwing with his mechanics, but I’ve been unable to find a link to that story. But the takeaway should be that he wasn’t healthy. He wasn’t able to generate the necessary power due to his hip issues and his velocity suffered as a result.

So the question becomes, if the surgery was a success and his rehab goes well, what can we reasonably expect from him for the upcoming season? Well that is definitely a tricky question since he’s almost 32, he’s two years removed from throwing in the 90s, and there’s the possibility that he won’t be back with the team that drafted him. I think in the best-case scenario we could see him start hitting his 2012-2013 velocity (~90.3 MPH) and if that’s the case we could start to see his K/9 creep up to around the 9.0 mark again. But that’s just my opinion and my opinion means basically nothing, so I’ll include a comparison.

I was only able to find one example of a pitchers who’d undergone the same type of surgery as Lincecum and that was Charlie Morton. In October 2011 he also underwent the hip surgery. You can check out his velocity chart below. He also had Tommy John the following June so if you’ll humour me and ignore the elbow issues you’ll see that his velocity over the 2011 season dropped from 94 to just under 92, only to return to 95+ after recovery from TJ.

Over the last two years Lincecum has amassed 0.4 WAR and made $35 million. There is no doubt that the Giants overpaid for his service over the last couple of years and I can’t see him getting anywhere near that annual salary. If we go by the market rate of ~$8 million/WAR, on a bounceback contract where a team expects a 0.5 WAR season we could see a contract in the ballpark of $4 million. Even that seems high to me; if I were to venture a guess I would put it around the $2-million mark with incentives. I’m definitely not saying he’s going to be the pitcher from five years ago, but a dependable 4th or 5th starter with the potential to strike out almost 200 batters sounds pretty awesome to me. You’ve always got to wonder if he’s got any magic left in him. Baseball is better with The Freak in it and hopefully he gets back on the mound soon.

This week we will be following up on our previous piece regarding Least Valuable Players. There we identified Hanley Ramirez and Pablo Sandoval as the two worst performers of the 2015 season. We are not fans of beating the proverbial dead horse, but given that this was just the first year of their respective contracts we were interested in figuring out just how bad these deals are shaping up for the Red Sox.  The short answer? It is bad. Like, Lucas Duda throwing to home in high-pressure situations bad.

As you might recall, during the 2015 season Sandoval accumulated a -2 WAR whilst Hanley finished up with a -1.8 WAR.  Even after that woeful start, the Red Sox are still on the hook for 5 more years and $89.4 million for Sandoval, and 4 years and a total $90.25 million for Hanley. Just let that sink in for a minute: 9 seasons of potentially below-replacement-level performance for $180 million. That makes the Barry Zito deal sound like a real steal.

For the sake of argument, they do not have to be that bad for the rest of the contract, do they? I mean, these guys were 3-win players just two seasons ago; maybe this was just a hiccup. Well, Steamer seems to partially agree with this logic and projects them to improve substantially. More precisely, it projects Sandoval to have 1.8 WAR and Hanley 2.2 WAR during 2016. Returning to these levels of performance is something, but is it enough to salvage these deals?

We replicate the player assessment analyses we used in our piece comparing offseason splurges in pitchers, just to figure out the net value of these deals.  We use Steamer’s 2016 projections as a starting point for WAR and then apply a player aging curve that goes as follows:  WAR increases annually by +0.25 for ages 18-27, stays flat for ages 28-30, decreases annually by -0.5 for ages 31-37 and lastly decreases annually by -0.75 for ages 38 and above. With regards to the market value of wins we start off at $8 million per win and we apply a 5% yearly inflation rate.

Even after considering the improvements suggested by Steamer, none of the 9 seasons controlled by the Red Sox would produce a net positive value, and overall the net loss of these deals comes at $79.4 million.

We ran the numbers, and in order for the Red Sox to recoup their investments, even after letting 2015 go down as a sunk cost, Sandoval would have to accrue 10.35 WAR for the rest of the contract (73% more than the projection), whilst Hanley would have to accumulate 10.56 WAR (82% more than the projection). This seems to be rather unlikely, especially when you consider that the Steamer projection already seems bullish, implying a 4 WAR improvement between seasons.

We wanted to test just how bullish this prediction is. We set out to find the past seasons most like the ones Sandoval and Hanley just endured and tried to identify how those players fared off the year after as well as for the rest of their careers.  We searched the last 30 seasons for players between the ages of 28 and 32, that produced a -1.5 WAR or worse in at least 400 PA, after accumulating at least 5 WAR in the previous two seasons. Namely, we were searching for players that had been performing at a high level, still in their prime or early phases of decline, which suddenly plummeted in performance.

Lo and behold the mother of small samples. We found just 5 players that met these requirements, 4 of them are already retired and one of them, well, one of them also plays for the Red Sox.  Out of these five players four of them improved after their decline season, the other one was out of the game. Out of the ones that improved, only one, Joe Carter, was able to meet the 4 WAR improvement inherent to the Steamer prediction, actually he was the only one that was better than replacement level after the decline season. So far Joe Carter has also been the only one able to play more than one season in the majors after the decline, with the jury still out on Allen Craig.

Just how good were Joe Carter’s first five seasons after the decline? Well he won back-to-back World Series with the Blue Jays, starred in one of the most memorable moments of baseball history and amassed a total of 9.4 WAR; a figure similar to what would be required for either Hanley or Sandoval to break even in their contracts. Just how bad is the alternative? Another season of negative WAR (-1 is the average for those not named Joe Carter) and 0 WAR from then on; a scenario like this would produce net value losses for the Red Sox close to $200 million or 150% more than what emanates from the Steamer scenario.

I know that we are dealing with extremely small sample sizes, but entertain this thought for a second. Let’s imagine that the above players represent the universe of possibilities and hence Pablo and Hanley each have a 20% chance of becoming Joe Carter and returning a net value of 0, that means breaking even and getting fair value for investment, and 80% of teetering off and producing a net loss of around $100 million.  Under that scenario the expected value of keeping both players comes somewhere at a net loss of $160 million over the life of the contracts. That is not necessarily crippling as it translates roughly to 3-4 lost wins per year, but these bad decisions can find a way to add up quickly.

Based on this, the Red Sox would certainly welcome another Dodger bailout, however this time around they might have to add additional value for a deal to go through. Moving forward the Red Sox might want to pursue one of three alternatives. First off, they might use that $160 million expected net loss value as an upper bound of how much they would be willing to send (in either money or player value) to another team as compensation for taking these contracts off their hands. Secondly, they could settle on the Steamer projection and set that upper bound on $80 million. Lastly, they could try to make the other team believers of the Joe Carter dream, and try to get away with not sending anything else, and even hoping to get something of value back in return, but this seems rather unlikely.  In theory by sending something (money or players) of less value than those upper bound figures to facilitate the deals they would be effectively cutting their losses.

With regards to the debate between sending some money or a player with value to make the deal work, it should be noted that $160 million in net player value over 5 years is something like Xander Bogaerts and a Top 25-50 prospect. Despite all the good will that recent deals have gained Dombrowski, there is no way Red Sox Nation would look kindly into giving up that kind of talent just to undo a mistake. The Red Sox are looking to become consistently competitive for the years to come and it does not make much sense to mortgage the team’s present and future by giving up so much controllable high-end talent. It may be time for the Red Sox to leverage their financial fortitude, bite the bullet, subsidize part of the contracts if need be, and move on.

After the announcement of the Cy Young and MVP winners, the award season is officially over and the offseason is in full stride. Most, except perhaps Royals and Mets fans, have moved on from the 2015 season and are focused on the year ahead. However, before doing so, I wanted to answer one final question about the past season: Who were the league’s Least Valuable Players (LVPs)?

Inspired by Neil Paine’s piece on Bryce Harper and the MVP I define the LVPs as position players (with at least 400 plate appearances) that not only had a bad year in terms of performance, but had an even worse performance relative to the salary they were being paid.

Why the distinction?  First off, most teams, with a few apparent exceptions (I’m looking at you Dodgers) have some sort of payroll limitation. Therefore having an expensive player stink up the place limits the opportunity that teams have to replace them via free agency or trade.

Secondly, it is my initial assumption, that underperforming players with large contracts may get disproportionally more playing time than similarly underperforming players with cheap contracts. This might be because teams hope that by giving players a chance to work things out at the plate they may salvage their initial investment or even entice another team to take a flyer with them. This, in the end, might be compounding the issue in the long-term as it robs the team the opportunity to try out existing farm-level talent at the position for instance.

It should be noted that it is possible that unlike most replacement-level players, underperforming players with big contracts were at some point actually good players and might have some other intrinsic value for their teams (i.e.: leadership, tradition, marketing, etc.) that justifies playing time; think of Ken Griffey Jr during his last few seasons or Derek Jeter’s farewell tour. However, for the intents and purposes of this article we will not be discounting player’s terribleness by any of these measures.

As far as methodology goes we will be replicating Paine’s approach from the previously mentioned article. FanGraphs calculates the monetary value of a player by estimating how much teams spent during the preceding offseason per projected WAR and then multiplying this value by accrued WAR during the season to get a sense of how much those wins above replacement would’ve cost in the “open market”. Then, from this “open market” value we subtract the actual salary (or rather salary cap hit from spotrac) of the players to get a grasp for their relative value or net value. In the case of over performing players this would turn into a value surplus for the team, whilst for underperforming players this would represent an additional cost for the team.

For example, per FanGraphs, the cost of a win in the 2015 offseason was approximately $8 million. Mike Trout accumulated WAR of 9.0 during the year, which means that the value his 2015 season was around $72 million. Meanwhile, his salary was a “mere” $6.1 million, which makes the surplus for the Angels somewhere around $66 million. In other words, the Angels paid $6.1 million in salary to get $72 million worth in production, which is a bargain of historic proportions.  Conversely, during the 2015 season Ryan Howard accumulated a WAR of -0.4, which translates into a -$3.2 million value. Not only that, but Howard was paid a cool $25 million for his services, which means that the true cost to the Phillies was of $28.2 million. In other words Philadelphia invested $25 million to get -$3.2 million in production, which over time is the kind of decision that leads to this.

So without further ado here are our Top 50 LVPs from the 2015 season:

The American League LVP is a tight race between two teammates in which Hanley Ramirez narrowly beats out Pablo Sandoval, even after failing to accumulate enough plate appearances to qualify for the batting title. Meanwhile, Ryan Howard stands head and shoulders above the competition in the National League specially after considering that the Dodgers heavily subsidized Matt Kemp’s salary.

When considering teams most affected by this subset of underperforming stars we can highlight the Red Sox and White Sox leading the way with over $60 million of net value lost each seriously shooting themselves in the foot as both had aspired to contend in 2015.  This was particularly damning for Boston; had they not had these terrible contracts on hand, and holding all else constant, the Red Sox would have finished with the 6th best positional net value in the AL, ahead of playoff teams like the Astros, Rangers and Yankees and with sufficient cash to spend to shore up their well-documented starting rotation deficiencies.

Lastly, it’s worth noting the vast number of players on the list that were signed as free agents, extended or traded for during the past year. All in all roughly half of the players on this list fit that description, which is something to keep in mind when your team announces its next big move during the coming offseason (uh-oh).

Note: This analysis is also featured in our emerging blog www.theimperfectgame.com

When I started this piece the Red Sox were going hard for David Price, but they hadn’t signed him yet, and it still wasn’t anywhere close to being a guaranteed deal. Now Price has signed with the Red Sox and Greinke is headed to Arizona (who saw that coming), so this piece is no longer realistic (as though it ever was). I still think it is interesting to think about however, so here you go.

Situation: Price signs elsewhere and the Red Sox are forced to pursue Greinke. As some have hypothesized, Greinke is inclined to stay in the National League so he can continue to hit. To sign him, the Red Sox will have to overpay by a significant margin. Either that or let him hit?

Okay, first a clarification: Greinke would not be the new DH (could you imagine replacing Ortiz on his retirement tour with Greinke). No, he would be given the opportunity to bat, say, in twenty-five of his starts next year (in the contract it would then be determined, the number of games Greinke would get to hit the following years, based on how he hit in the previous year). A few of those starts will be inter-league games, possibly a couple of them would be to give Ortiz a day off, but mostly Ortiz would hit for another player on the field. You might say that wins are money and that since this would cost you wins you should just cough up the money to get Greinke to come to Boston. But what if this would hardly cost you any wins at all? What if the team could be just as good when Greinke hit?

The first thing to do is to find out how good Zack Greinke has been at hitting. In hit National League career he has a wRC+ of 67, though over the last three years he has seemed to improve, hitting to a wRC+ of 87. To give a comparison, Madison Bumgarner, known as a very good hitting pitcher who has even pinch-hit on occasion, has a career 49 wRC+ and a 73 wRC+ over the last three years. It does seem like Greinke has improved, hitting-wise, lately, but he is a pitcher, and you can’t expect too much out of him, so we will take his median wRC+ over the last three years, 74, as a reasonable true talent. So this was a starting point to compare this to other players.

The first name that seems interesting is Ryan Hanigan. Unlike some other Red Sox players who haven’t hit that well of late, no one really expects him to improve. Over the last three seasons he has a wRC+ of 75. This is basically equal to Greinke’s prescribed true talent, so it seems as though we could break even. Against righties however, Hanigan had a wRC+ of 69 over the three-year span, and it was even worse last year at 62. So, it seems that against righties, Greinke, whose wRC+ hardly drops in his three-year sample size, might even be the better hitter. Additionally, Greinke is actually the better baserunner as well. Over the last three years, Greinke is the best baserunning pitcher in baseball, with a BsR score of 0.5, slightly above average. He joins only four other pitchers as above-average baserunners over that time period. Hanigan on the other hand is a below-average baserunner, with a BsR of -4.0 over the last three years. This is just another reason for Greinke to hit instead of Hanigan.

If Hanigan really was the backup catcher, then, assuming Greinke was the better hitter against righties, the Red Sox would already be close to working out a good way for to hit Greinke. Hanigan would become Greinke’s personal catcher, and Greinke would hit instead of Hanigan against righties. Hanigan though is only the backup catcher until Vazquez is healthy. While he has had a setback in playing winter ball, he is expected back early next year. So Greinke hitting for Hanigan may only be an option for around a month.

Vazquez, Castillo and Bradley are all other options to hit for against righties. Bradley and Castillo have had 65 and 69 wRC+’s against righties, respectively. These would seem like great options to pinch hit for, except for the fact that they are supposed to be good candidates to improve. Depending how they do early in the year, both could be able to be hit for by Greinke, though it wouldn’t be great for their confidence. Vazquez actually hit slightly better against righties in his very limited tenure in Boston, though that could reverse itself, since he is right-handed. So it seems as though the Red Sox can piece together 10-15 games where, because of personnel or days off, it makes enough sense to have Greinke hit against righties. They still need 10-15 more starts however.

This brings us to facing lefties, and Pablo Sandoval. Last year was a bad year for Sandoval, but even before that he was bad against lefties. Over the last three years he has had a wRC+ of 61 against lefties. Last year it was even more horrendous, however, as he had a score of 21. Pair this with his -15.1 Def rating last year, and it seems obvious he should not be playing third base with a righty on the mound. The Red Sox may sit Sandoval against lefties anyway, but this could be an opportunity to hit Greinke and get better defense. If the Sox were planning on having Greinke hit, they would do well to try to find a cheap, good-fielding third baseman. If the Red Sox could find someone like Gordon Beckham, who is an inexpensive, above-average defender at third base, that would be a big upgrade on defense. He had a positive 5.5 Def rating playing mostly third last year, and cost the Braves only $1.25 million plus incentives. There are probably countless others, though, that could fill this role.

Sandoval had a wRC+ of 99 against righties last year, so it doesn’t seem like he would be hit for against righties, but if his defense or hitting doesn’t improve, he could be a candidate to be hit for once in a while, even against righties. This is especially the case, given the importance of third base defense when Greinke is pitching. Looking at Greinke’s batted ball spray chart, you see a lot of ground balls. Focusing on those ground balls, you can see third base got a decent amount of attempts, especially ranging left and right, which is Sandoval’s weakness. So, at the rate Sandoval played last year, though he is expected to improve at least slightly, Greinke could hit for him all the time, with a good defender at third.

One other thing that should be mentioned is what will happen once Greinke leaves the game. When bullpen pitchers come in, they would then occupy Greinke’s batting spot. This is not as big an issue as it may appear, because bullpen pitchers will usually get pinch-hit for, but it would force Farrell to manage more of a National League game. It is unfortunate that the DH cannot be moved around in the batting order like every other position, but the Red Sox would just have to make use of their fairly deep bench.

Additionally, since 2010 the National League has only scored around six fewer runs per year than the American league from the seventh inning onward. This is the time in the game when the starter is generally out of the game, so National League teams can pinch-hit for their relievers. Since Greinke would only bat in 1/6th of the games, this averages out to be a run per year, or about 1/10th of a win. It could be even less than that since the Red Sox have a quality bench. So the disadvantage of having bullpen pitchers with lineup spots seems to be incredibly minuscule, even almost unnoticeable.

On average, around a third of pitchers are lefty. If the Red Sox try to line up Greinke against lefties, you can probably assume that it will happen around a dozen times. If Greinke gets six starts in April, he will probably face a righty in about four of those games. Greinke will bat for Hanigan on those occasions and for Sandoval against lefties, leaving nine more batting days for Greinke. The Red Sox could probably find nine more days to both take the defensive upgrade and have Greinke bat, rest a slumping player, or simply give starters a day off here and there. These might be slight disadvantages for the team, but could possibly be neutralized with the possible upgrades when Greinke bats for Hanigan and Sandoval.

Overall, if Greinke continued to be the great pitcher he is, a great offense would not be as important to the Red Sox on his pitching days. While on the whole it is probably a slight disadvantage to have Greinke bat, it seems to be very small, and not worth shelling out a few more tens of millions of dollars. The biggest question mark in this is Greinke’s true batting talent. While he has hit well in his career, he has totaled less than 400 plate appearances over his entire career. Anything could happen in this small sample size, so it is hard to know. This probably will never happen, but it is interesting to think about. For now though the Red Sox have Price, and the Diamondbacks just shelled out $206 million for Greinke, so the Red Sox are just as well not even thinking about him hitting in Boston.

We might as well wait until 2020, when Bumgarner will hit the open market, until we think about this again.

In my last article on these here internet pages, I attempted to create the best conceivably possible player by taking the most valuable seasons in baseball history and putting them all together into one awesome player. The results were fairly ridiculous. Can we get equally ridiculous results from the greatest age-seasons from pitchers? I’m going to make a couple of tweeks to my methodology from last time to tailor it more towards pitchers, who, at the extremes, seem to have more peculiar career arcs.

• No pre-1900 seasons

I included this rule for position players because I’m not too familiar with the era. I’m including it for pitchers because they weren’t doing anything like what modern day pitchers, or even pitchers in the 1920s were doing. I mean, in 1889, John Clarkson of the Boston Beaneaters pitched 620 innings with a 2.73 ERA. His FIP-WAR was 10.9 while his RA9-WAR was 19.7. That is 4.7 WAR better than Babe Ruth’s best season.

• No seasons with less than 50 IP

This doesn’t really apply all that much, unless you want me to include Joe Nuxhall’s two-thirds of an inning in 1944 when he was 15. He allowed 5 runs on 2 hits and 5 walks.

• No duplicates, take player’s best season

This wouldn’t be as much fun if it were just young Dwight Gooden and old Randy Johnson

As for which version of WAR I’m going to use, FIP-based WAR seems to be less prone to wild fluctuations, particularly with old-timey players. The gradual increase in strikeouts over the past century-plus seems to have balanced out the gradual decline in innings, so that the ridiculous seasons of today are similar in value to the ridiculous seasons of yesteryear. With all that said, let’s get to it and create a Ridiculous Moon Wizard Pitcher.

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