(Visited 10 times, 1 visits today)FacebookTwitterPinterestSave分享0 If a report on EurekAlert is right, some evolutionary biologists used lack of evidence for natural selection as confirmation for evolution. They predicted guppies would show no evidence of a “grandmother effect” on life history after reproduction, and “that is what they found.” The question under study is why evolution keeps aging individuals living if it’s only reproductive fitness that matters in keeping a species going. Perhaps the aging are worth keeping around if they contribute to the fitness of the offspring (see 07/23/2003 entry). After admitting that the “granny effect” is not found in many mammals, even among sociable groups, the article said:Since guppies are livebearers that provide no postnatal maternal care, Reznick et al. predicted the populations would show no differences in postreproductive lifespan–which is what they found. Though overall lifespan varied among the populations, these variations stemmed from differences in time allotted only to reproduction. Postreproductive lifespan, in contrast, showed no signs of being under selection, and appeared to be what the authors called a “random add-on at the end of the life history.” Random or not, this is the first demonstration of a postreproductive lifespan in fish. (Emphasis added in all quotes.)The article then stated that whether postreproductive lifespan can be under selection at all is an open question. But then, it said that this new study helps gain an evolutionary perspective on such matters – including how they relate to humans.What kind of reasoning says, “we predict there will be no evolutionary natural selection” on a process, then uses the confirmation of the prediction as evidence for evolution? You can’t have it both ways. The article stated an evolutionary principle: “For natural selection to shape the twilight years, postreproductive females should contribute to the fitness of their offspring or relatives.” Notice that word should. If natural selection is the be-all and end-all of existence, and if nothing makes sense except in the light of evolution, and if most biologists expected there to be a granny effect, then Reznick’s study amounts to falsification. Grandparents everywhere should be relieved that another evolutionary principle has been falsified, because now their self-worth does not need to be tied to their tubes. You can’t bet at the racetrack that an aging Charlie Horse will win because it is more fit, then claim his loss also confirms your prediction. Charlie Horse is not just a loser; he’s a pain in the arm of science. Charlie’s hoarse cries for his theory to get to the finish line, or even past the starting gate, are increasingly falling on deaf ears among those who know how to spot winners and losers (see 09/26/2005, 08/15/2005 commentaries).
Why Tech Companies Need Simpler Terms of Servic… Related Posts bernard lunn This is one post/chapter in a serialized book called Startup 101. For the introduction and table of contents, please click here.There are two schools of thought about founders as CEOs. One school says that founders rarely make good CEOs: the skill sets are simply different. The founders may make more money in the end, but they need to hire professional CEOs. The poster children for this school are Sergey Brin and Larry Page as Google’s founders, with Eric Schmidt as CEO. The other school says that no one has as much passion, drive, and deep market and technological understanding as the founder, and so they are best off remaining as CEO. The poster children for this school are Bill Gates and Larry Ellison. As a founder, which school of thought do you belong to? If you have a point of view, how do you make sure your point of view prevails? Above all, make sure you at least have a point of view.Three Classic ScenariosTwo equal founders + one new CEOThis is what happened at both Yahoo and Google. (Yes, the Google story is different in most other ways, and no need to rehash Yahoo’s later mistakes.) This seemed to work for them. The two founders made good money and avoided a lot of work they did not understand or enjoy. It also avoids the issue of which founder should become the boss.Triumphant returnThis has one blazing success story: the return of Steve Jobs to Apple. But other ones did not work out so well: Jerry Yang at Yahoo comes to mind.One partner who emerges as CEOBill Gates emerged as the leader of Microsoft, not Paul Allen.Which Scenario Do You Want to Play Out?Some founders have no doubt. They fall clearly into one camp or another. They say either…“No way in hell is anyone else running my business. Back off. Outta my way!”Or…“Who wants to do all that boring stuff anyway. Let me do the creative stuff. and let someone else make money for me.”If you don’t have such clarity, you will need people whom you trust and who know you well to give you an honest assessment. And you will need to do some soul-searching.Your decision will depend on many factors, mostly personal ones. You could hire for any skill-set you are missing. You might want to re-read the early chapter “Are You Really an Entrepreneur.”It’s Different When the Game ChangesMarkets can change. Let’s say you are a techie, and your venture was set up as a consumer website but then morphed into a B2B venture, for which sales skills are paramount. Or vice versa.In such circumstances, you may be smart to say, “I need someone else at the helm.”Don’t Let Investors Make This DecisionThis is your decision. Some investors have a very firm view on this. Some fall into one school of thought or the other. However, some are agnostic, letting circumstances guide their view. Knowing their view before you sign the term sheet would be good, to make sure you both see the world in the same way.If the VC has a strong view that founders never make good CEOs, and the founder thinks, “No way is anyone else running my business,” then get ready for one big bruising fight! A Web Developer’s New Best Friend is the AI Wai… Tags:#Gritty Entrepreneurs#NYT#start#StartUp 101 Top Reasons to Go With Managed WordPress Hosting 8 Best WordPress Hosting Solutions on the Market
About the authorPaul VegasShare the loveHave your say Chelsea ace Hazard: I’ve changed the approach to my careerby Paul Vegas10 months agoSend to a friendShare the loveChelsea ace Eden Hazard admits he’s taken better care of his body in recent years.Hazard has been with Chelsea since 2012.”Football is my job, but I enjoyed it 20 years ago, I enjoyed it 10 years ago and I enjoy it now. In that respect I haven’t changed a lot.”I prepare for games exactly the same as I did when I was younger. But now the difference is that I am getting older and I need to take more care of my body. I work a bit in the gym with the physio. Five years ago I just enjoyed training and then I went home.”Now I take my time. Recovery sessions are important for me now. That’s the big change, but after that I’m the same guy, with the same happiness on the pitch.”
TagsTransfersAbout the authorPaul VegasShare the loveHave your say Chelsea ACCEPT fourth bid from Bayern Munich for Callum Hudson-Odoiby Paul Vegas10 months agoSend to a friendShare the loveChelsea have accepted a fourth bid from Bayern Munich for Callum Hudson-Odoi, it has been revealed.Sky Deutschland says Bayern’s offer of €40m has seen Chelsea agree terms over the 18 year-old’s sale. The decision being made just hours before Hudson-Odoi shone in defeat last night at Tottenham for the first-leg of their Carabao Cup semifinal.The sticking point now is whether the teen leaves for Bayern immediately or whether he plays out the season in England before joining the Germans in June.For their part, Chelsea would prefer Hudson-Odoi stay given his recent contribution to manager Maurizio Sarri’s team.
BOSTON – That ordinary bottle of juice delivered to your doorstep will set you back at least $55. But the bag of marijuana that comes with it? On the house.Retail marijuana stores are months away from opening in Massachusetts, but some companies have been quietly operating for more than a year, selling and delivering marijuana via a legal loophole that exists in nearly every state that has legalized recreational marijuana use.Companies like HighSpeed, which describes itself as a juice delivery service, are exploiting so-called “gifting” provisions that allow for the exchange of small amounts of the drug, so long as it’s given away — “gifted” — from one adult to another.The legal language makes it permissible to pass a joint at a party or drop a bud in your brother’s Christmas stocking, but some entrepreneurs see it as an opportunity to get ahead of the regulated market, planting an early stake in what could become a crowded and lucrative industry.In places where legal pot shops exist, gifting operations undercut the licensed retailers, because they don’t face the same oversight or pay marijuana sales taxes. And they complicate things in places like Vermont, Maine and Washington, D.C., which have legalized pot but have no firm plans to open regulated storefronts.“Under any fair reading of the law, these businesses are illegal,” said Roger Katz, a Republican state senator in Maine who is studying the issue. “If it walks like a duck, quacks like a duck, it is a duck.”At least four enterprises have done gifting business in Massachusetts since marijuana was legalized in December 2016, two of them in the Boston area, The Associated Press found in an investigation that included records gathered from law enforcement agencies around the state.In addition to HighSpeed, a Boston-area company cleverly called Duuber has drivers delivering marijuana-themed T-shirts that come with gifts of pot.Officials in western Massachusetts also looked into a Craigslist ad offering plastic sandwich bags costing up to $325 apiece (the marijuana in them was free) but dropped the case after they couldn’t identify the seller.In Springfield, officials ordered a smoke shop called Mary Jane Makes Your Heart Sing to shut down last March after it gave marijuana to customers who paid a $25 to $50 admission fee.That hasn’t scared HighSpeed, which also operates in D.C.“We’ve had no issues with law enforcement, and we’re going to do our best to keep it that way,” said founder David Umeh. “We’re not doing anything wrong. We’re abiding by the current legislation until it changes.”Gifting provisions are on the books in Massachusetts and all but one of the other states that have legalized marijuana: Alaska, California, Colorado, Maine, Nevada, Oregon and Washington state, plus D.C. Most instituted the measure specifically as part of new marijuana laws.Vermont does not have a provision, but local experts and activists argue the exchanges will be permitted there, too, since they’re not expressly banned.Some states have tried to stem abuse of the laws by prohibiting businesses from advertising marijuana giveaways or specifically banning “delayed or disguised” payments for marijuana gifts, said Leo Beletsky, a law professor at Northeastern University in Boston.But businesses simply find ways to obscure what they’re doing, he said, and then rely largely on word of mouth to make sales. Clued-in customers can infer how much pot they’re ordering judging by the price and size of the items accompanying it, but for the most part, they’re at the mercy of the seller.In the case of HighSpeed, there is no mention of marijuana on its website. The company sells drinks priced from $55 to $150, depending on whether the beverage comes with “Love” or “Lots of Love.”The AP recently put in a $60 order for “Raspberry Roxbury” with “Love” and received a bottle of Tazo juice along with about an eighth of an ounce of marijuana.Duuber also doesn’t explicitly spell out its marijuana “gift” on its website. But when the AP ordered a $100 product listed as “Luxury Tshirt – Citrus – small,” the brown paper bag delivered by a driver contained a white T-shirt with the company’s name in black over an image of a marijuana leaf — and a clear plastic bag of marijuana labeled “1/4 Ruthless OG.”The opening of retail shops in states with marijuana laws should eventually make most gifting operations obsolete, said Morgan Fox, spokesman for the D.C.-based Marijuana Policy Project.“People want quality control-tested products,” he said. “The sooner that happens, the sooner this sort of thing disappears.”But in Colorado, where pot shops opened in 2014, gifting businesses are still hatching creative ways to skirt the law, said Detective Kerry Linfoot of the Colorado Springs Police Department. The department shut down 14 gifting businesses last year.U.S. Attorney General Jeff Sessions’ decision to rescind an Obama-era policy that called for non-interference with legal state marijuana operations could also help bolster gifting and other underground operations, Beletsky said.“If the feds somehow came down on state regulators or licensed retail operations,” he said, “that could provide a convenient opening for these grey-market operators to scale up what they’re already doing.”___Follow Philip Marcelo at twitter.com/philmarcelo. His work can be found at https://www.apnews.com/search/philip_marcelo.
NEW DELHI: Sumitra Mahajan, the outgoing Lok Sabha Speaker, is the latest BJP veteran to declare that she will not contest the national election starting next week. Making her resentment clear in a letter released by her office, the senior parliamentarian from Madhya Pradesh said she had “freed her party” from a difficult choice.While announcing candidates for Madhya Pradesh, the BJP had held off on naming anyone for Indore, which had left its long-time representative Sumitra Mahajan in uncertainty for weeks. “There were speculations, and so I decided to end them and free the party to make its choice. I will not contest the Lok Sabha elections,” she said. Mahajan, however, maintained that she would continue to work for the party and campaign for it. “The party (BJP) had not been able to name a candidate (from Indore Lok Sabha seat) for so many days, and there were speculations if someone over 75 years of age will be fielded. So I decided to end all this,” she said. Bharatiya Janata Party (BJP) president Amit Shah had in an interview to The Week magazine on Thursday stated that it was his party’s decision not to give Lok Sabha poll tickets to those above 75 years of age, leading to veteran leaders like L K Advani and Murali Manohar Joshi being unceremoniously dropped. Though he had not named Mahajan, the outgoing Lok Sabha speaker also falls in the same age bracket. Mahajan was first elected to the Lok Sabha from Indore in 1989 and has won all the seven consecutive elections. She was a minister of state in the Atal Bihari Vajpayee government, holding portfolios of human resources development, telecom and petroleum between 1999 and 2004. When the BJP swept to power again in 2014, she was named the Speaker of the Lok Sabha. Mahajan also issued a short press statement questioning why the party has not been able to name a candidate till now. “Possibly the party is hesitating to take a decision. I had discussed this issue with senior party leaders and left it to them to take an appropriate decision,” she said, adding, “It seems they still have some doubts in their mind. So I declare that I do not wish to contest the Lok Sabha elections.” The party, she said, can now make its decision without any hesitation. Stating that she had received immense love and support from people of Indore, she hoped the BJP would quickly make up its mind for its candidate from Indore and end this uncertainty.
After one of the most astonishing score lines in the history of the World Cup on Tuesday — Germany 7, Brazil 1 — nothing that happens in Sunday’s World Cup final would be a total surprise. But we do have estimates of the most likely final scores for the game.Germany is a 63 percent favorite to defeat Argentina, according to the FiveThirtyEight forecast. Argentina had a slightly higher Soccer Power Index (SPI) rating when the tournament began, but Germany has seen its rating rise, particularly after its thrashing of Brazil, and it now ranks No. 1 by some margin. Betting lines also have Germany favored.The SPI match predictor allows us to predict the number of goals scored and allowed for each club. It calls for 1.7 goals by Germany and 1.2 by Argentina.There are a couple of problems with this — for one thing, a team cannot score seven-tenths of a goal. So the match predictor uses a version of a Poisson distribution, which calculates the probability of the teams finishing with any whole-number score. For example, if Germany scores an average of 1.7 goals, how often does it score exactly two goals or exactly three goals? That’s what a Poisson distribution does.Another issue is that the match predictor is calibrated on the basis of 90-minute matches when knockout-round games can go to extra time. To account for extra-time results, we ran an additional Poisson regression based on the results of extra-time games in major international tournaments since 2005. (In geek speak, we’re nesting a Poisson distribution within another Poisson distribution.) All of that produces the following heat map:Read left to right for Germany’s score and top to bottom for Argentina’s. Boxes in which the score is still tied after extra time represent cases where the game goes to penalty kicks (there is about a 14 percent chance of this happening). The 10 most probable scores are as follows:Germany 2, Argentina 1Germany 1, Argentina 0Argentina 2, Germany 1Germany 2, Argentina 0Argentina 1, Germany 0Germany 3, Argentina 11-1 draw (game goes to penalties)Germany 3, Argentina 2Germany 3, Argentina 0Argentina 2, Germany 0What are the odds of another 7-1 scoreline? The model says there is only about a 0.06 percent probability of such a score favoring Germany (about one chance in 1,600). There’s even less of a chance — more than 10,000-to-1 against — of the same score favoring Argentina.But these figures may underestimate the chance of astonishingly lopsided results. The mathematical basis for the Poisson distribution is the assumption of independent trials. This is a little inexact (it describes a special case of a Poisson distribution called a binomial distribution), but a Poisson distribution is treating a soccer game something like this:Suppose we expect Germany to score 1.7 goals on average in a 90-minute game against Argentina. That translates into about a 2 percent probability (1 chance in 50) of scoring a goal in a given minute of play.So we can run an experiment where we randomly draw ping-pong balls from a set of 90 lottery machines, one representing each minute of the game. In each machine, there are 50 balls, one labeled GOAL! and 49 blanks. The probability of drawing a GOAL! from one machine doesn’t affect what happens with the next one. (This is the assumption of independent trials.) After we’ve drawn balls from all 90 machines, we count the number of GOAL! balls. This represents how often Germany scored in the game.We can repeat the experiment a bunch of times. Most commonly, we’ll wind up with something like one or two GOAL! balls. But other times we’ll have drawn zero or four or six. The relative frequency of these outcomes represents the Poisson distribution for Germany’s score.As strange as this experiment might seem, it isn’t a bad mathematical approximation of a soccer game. And for the most part, Poisson distributions do a good job of modeling real-world soccer scores.But there are some complications. For instance, we may have some estimate of how the absences of Neymar and Thiago Silva might affect Germany’s chances of scoring against Brazil. But there is some uncertainty around that: Maybe Brazil plays more fluidly when it isn’t waiting around for Neymar to do something, or maybe it breaks down. This is equivalent to not knowing exactly how many GOAL! balls and blanks there are in the ping-pong machines. This uncertainty will tend to slightly increase the number of extreme outcomes (Germany scoring zero goals or a lot of goals) that we observe in the real world.Another issue is that the texture of play in soccer depends to some extent on the scoreline. Play is usually tighter and more conservative in a drawn game and then opens up once the tie is broken. As a result, standard Poisson distributions slightly underestimate the chance of draws and of some wild scores, such as 5-2. (The variant of the Poisson distribution that we use is meant to address this problem.)For the most part in sports, these complications are not worth worrying about. There are cases where a Poisson distribution or a normal distribution isn’t perfect — normal distributions seem to slightly underestimate the number of extreme outlier scores in sports — but they usually hold up reasonably well. Nobody gets hurt when you say that Germany has only a 1-in-4,000 chance of winning by six goals when it actually had a 1-in-400 chance.But real-world distributions are often slightly fat-tailed, meaning that extreme outliers happen more often than the normal distribution predicts. And — outside the sports world — using the wrong model can cause real problems, underestimating the chance of an earthquake or a financial crisis.
Quick — which NBA player is most integral to his team’s offense? Which player shoulders the biggest offensive burden? And to what degree are those questions even equivalent?Statistically, such concerns fall under the umbrella of “usage rate,” a term that colloquially describes an entire class of metrics tasked with quantifying the size of a player’s offensive role. Usage is one of the most accessible concepts in basketball analytics — rock-simple in its purview and relatable to anyone who’s ever played with a shameless ball hog or been a terrified freshman playing hot potato. In statsier circles, usage is a staple of player analysis, in part because it remains relatively constant amid a player’s shifting contexts and roles. At a glance, usage says more about how a player plays than most other basic basketball metrics.One small problem: Nobody seems to agree about what exactly usage rate is, or should be, or how it is calculated. Many analytics-minded observers don’t even know there are different, competing versions of the statistic in popular use, much less that each variant has its own philosophy about what it means to “use” a possession. For a term so common to the modern hoops lexicon, that’s more than a little strange. So let’s have ourselves a little history lesson and learn much more than you ever wanted to know about usage rate, in all its permutations.Usage through the yearsLike many concepts in basketball analytics, usage rate can be traced back to Dean Oliver and John Hollinger, still probably the field’s two most influential figures. The notion that too much (or too little) offense could flow through an individual player is as old as the game itself, but it’s hard to find anyone formally putting a number on the phenomenon before the early-to-mid-2000s, when Hollinger published his inaugural “Pro Basketball Prospectus” and Oliver wrote the seminal “Basketball On Paper.” In fact, the thought of listing a player’s rate of possession-usage at all — let alone as something other than a purely negative indicator — was alien to many of the early hoops number-crunchers.To understand why, it’s useful to look back at the primordial era of basketball metrics. NBA statheads cribbed many of their early concepts from baseball’s sabermetric movement — which effectively had a 25-year head start — including a tunnel-visioned focus on maximizing efficiency. Such a fixation makes sense in baseball, where a player’s susceptibility to making outs is unambiguously negative — you get 27 of them each game, to be guarded vigilantly — and you can draw a straight line between a player’s individual efficiency and his effect on the team. Hence the reasoning, as applied to basketball: If possessions, like outs, are the sport’s fundamental unit of opportunity, why would we celebrate a player’s propensity for using them up?Basketball is more complicated than baseball, however. Possessions alternate between teams, so at least one player must always have a hand in “using” each of them. More importantly, teammates do not take turns with their opportunities like hitters going through a batting order: Any individual player is free to use as many (or as few) of the team’s possessions as he wants. This provides a lot of complex ways for an individual to help the team beyond his own personal efficiency statistics.One of Oliver and Hollinger’s key insights was that the frequency with which a player generates offense — as proxied by usage rate — is a consideration that should always accompany (and temper) his efficiency metrics. “Some guys … are great shooters and passers, and rarely turn the ball over,” Hollinger wrote, introducing usage in the 2002 edition of his “Prospectus,” predicting the wars he’d fight over Carl Landry half a decade later. “If that’s the case, why don’t people regard them as superstars? The reason is that they cannot create their own shot as often as some other players can.” Usage rate was born out of the effort to quantify said ability to create.Hollinger’s original conception of usage, which can still be found at ESPN.com today, was a relatively simple pace-adjusted rate of shots, assists and turnovers per 40 minutes. Oliver’s, while rooted in the same basic tenets, went to a far more complex place, accounting for the additional possession-extending nature of offensive rebounds and even parceling out fractional credit to the scorer and passer on an assisted basket. But at their most elemental, both attempt to individually account for all the actions that can spell an end to any team possession: made baskets, misses that aren’t rebounded by the offense, free throws and turnovers.Neither Oliver’s nor Hollinger’s interpretation of usage, however, is the preferred version of 2015’s stathead. (At least, not according to this unscientific Twitter poll I conducted Tuesday.) Among the respondents who actually recognized differences between various flavors of usage, nearly twice as many said they use the Basketball-Reference.com (BBR) version as Hollinger’s. (Oliver’s version isn’t widely available online, except for college players.)As the stats are used today, there isn’t much separating the three. Mention that a player’s usage rate or usage percentage is in the high-20s to low-30s and you call to mind a ball-dominant focal point of an offense; drop down an octave, into the low-to-mid-20s, and you instead have a player who creates a good deal of offense but doesn’t dribble the leather off the ball. Whichever version you prefer, usage is in common enough usage that it serves as shorthand for offensive hierarchy.In most every practical application, breaking one or the other down to its atomic particles and recompiling them into the competing version will be pointless; you already get the idea. Still, it remains worthwhile to understand the differences, such as they are, and how those differences inform what it is you’re looking it. Why? Because BBR’s usage metric doesn’t include assists.Confusion reignsFull disclosure: I used to work for Sports-Reference, the company that runs BBR, so I’m close to the situation. And now, a scene from my former life running the company blog at a time when BBR founder Justin Kubatko and I staged nerd fights about this (and other statistical barnacles):ME: “Why do we use Hollinger’s definition of usage instead of Dean Oliver’s?”JUSTIN: “That’s not Hollinger’s. That’s mine.”ME: “It’s not what he uses at ESPN? I thought it was the same definition.”JUSTIN: “No. His multiplies assists by a third.”ME: “I see. But I guess the question still stands.”JUSTIN: “Mine is basically percentage of team plays used. What the heck is his actually measuring?”ME: “It’s trying to measure possessions, and failing. But Oliver’s formula gives us real possessions.”JUSTIN: “They’re not real, either! They’re estimates — better than Hollinger’s, but estimates.”ME: “I’m confused. This is Hollinger’s fault.”For most players, this distinction is largely irrelevant; among qualified players1Minimum 400 minutes. this season, the correlation between BBR usage and Oliver’s more full-bodied formula is 0.98. But for certain types of players, it can matter: It’s the difference, for instance, between claiming that DeMarcus Cousins carries the league’s biggest offensive burden (as he does under BBR’s formula) and giving the distinction to Russell Westbrook (No. 1, according to Oliver and Hollinger). One measures pure scoring affinity; the others factor in ballhandling responsibility while still strictly accounting for the player(s) who served as the conduit for every possession’s end.Neither approach is perfect. Playmaking is obviously a massive part of “creating” offense, and cutting it out entirely isn’t ideal. But just stapling assists onto a scoring metric misses huge chunks of what you’re trying to capture. Plus, heavy ballhandlers tend to have higher turnover rates than would be predicted from how often they end possessions, which suggests that even a completist accounting method such as Oliver’s is missing some fundamental aspect of how passers create shots for others.So with the advent of player-tracking data from SportVU, Seth Partnow of NylonCalculus.com set out to detect the invisible. He developed a statistic called True Usage, which incorporates “assist chances” (so-called “hockey assists,” plus passes that would have been scored as assists if the shot had been made) into the usage mix. The resulting leaderboard is decidedly skewed toward point guards and other primary ballhandlers, like LeBron James. If we’re truly interested in measuring a player’s offensive burden, that probably makes for a more accurate usage framework.The problem, of course, is that the old-hat usage figures are now entrenched in not only the analytic lexicon, but also the updating leaderboards on big industry portals like Basketball-Reference and ESPN. It’s hard to change hearts and minds without first winning over the APIs.From one stat to manyThen again, maybe the entire concept of a one-number “usage rate” has outlived its usefulness, particularly in an age of hyper-detailed SportVU possession stats. We can now see how long a player holds the ball, how often he passes, how many points those passes create — every conceivable piece of the puzzle is out there, if you know where to look. And just about every basketball analytics expert I consulted told me that they preferred a modular approach to usage, with different formulas to measure different aspects of a player’s offensive responsibility.“I don’t use just one usage stat,” Oliver told me. “I do have a shot usage, a field goal usage, and a possession usage stat. Depending on the question being asked, I will look at the one that makes the most sense.”Jacob Rosen, who writes about analytics for Nylon Calculus and the Cleveland sports blog Waiting For Next Year, concurs that today’s all-in-one usage metrics are inadequate. “Like any type of basketball stat, it’s the balance of wanting to push everything into one metric,” Rosen said. “In some ideal world, you’d have a stat that measures the dimensions of possession time, passes, potential assists, turnovers, shots, free throws, etc. But they’re on somewhat different planes of existence.”As a possible alternative to a one-size-fits-all usage formula, Rosen wondered if usage rate’s next step would be to incorporate player typologies, such as the Position-Adjusted Classification (PAC) system developed by current Cleveland Cavaliers Director of Analytics Jon Nichols. “In my mind, having those different dimensions would be more accurate,” Rosen said. “You could perhaps do a PAC definition just with usage-based things alone (i.e., passing, possession, turnovers, shots).”Given the state of today’s tools of observation, Partnow’s True Usage may have struck the best balance between the all-encompassing and the customizable, if not the most widely used and understood.“To me the ideal is True Usage,” Nylon Calculus writer2And FiveThirtyEight contributor. Ian Levy said. “It is as accurate a measure as there is of the quantity of a player’s offensive responsibilities. But the real benefit is that you can parse out the different components to see what comes from playmaking, scoring, turnovers. That’s the ideal — [a] good holistic measure [that’s] also parsable into components for descriptive uses.”If so, maybe we should all just turn our attention toward rebranding campaigns for the other myriad versions of usage rate — “Possession Rate”? “Scoring Attempt Frequency”? — or pester the bosses at ESPN or Basketball-Reference for one more column in the Advanced Stats tab. That is, until basketball’s next data revolution comes and brings with it an even more accurate way to measure offensive workload … which we can promptly christen “usage rate” and start all over again.
A homegrown WAR rate of 43 percent is well below the long-term average of 63 percent for world champs, but that number is propped up by teams that won their titles before MLB’s modern era of free agency and mass player movement. Since free agency began in 1976, the average champion got about 50 percent of its WAR from homegrown players. In comparison with the highly imported nature of the 2004 Red Sox roster, the 2016 Cubs had a pretty normal mix of developed and acquired talent.Finally, the quality of the 2016 Cubs’ position players set them apart from the 2004 Red Sox, particularly on defense. Both teams received immense contributions from their respective pitching staffs; Boston ranked 14th among champions in pitching WAR,4Per 162 games. while Chicago ranked 27th. But the Cubs’ lineup also generated the 16th-most WAR by a championship team, while the Red Sox got only the 77th-most WAR of any champion from its lineup. Some of Chicago’s impressive young position-player talent flowed from a promise Epstein made at his introductory news conference in 2011. There, Epstein declared his intention to build “a foundation of sustained success” rooted in player development, echoing a similar sentiment from early in his tenure with Boston. “We’re going to turn the Red Sox into a scouting and player development machine,” he said in 2002. Although the returns didn’t come in quickly enough for the veteran Red Sox of 2004 — only 12 percent of the team’s WAR was generated by players who began their careers in Boston, the third-lowest rate for a champ ever — Epstein’s machine did eventually produce younger, more homegrown champions in 2007 and 2013. Epstein left Boston in 2011, but his fingerprints were all over the roster that brought Boston its ’13 title. And in 2016, 43 percent of the Cubs’ WAR was generated by players who made their MLB debuts in a Chicago uniform, many of whom Epstein drafted himself. When Theo Epstein left the Boston Red Sox to become president of baseball operations for the Chicago Cubs in the fall of 2011, he told reporters he was “ready for the next big challenge.” And what a challenge it was: The Cubs were coming off of a 71-win season, without much help on the way. Famously, the team’s last pennant had come 66 years prior, and it hadn’t won a World Series in 103 years.Epstein, of course, was well acquainted with the anguish of a supposedly cursed fan base. In 2004, as general manager of the Red Sox, he’d been the architect of Boston’s first world championship in 86 years. The parallels to Chicago’s plight were obvious. But the prospect of a second Epstein miracle seemed too much to realistically expect. The 2004 Red Sox had needed one of the greatest comebacks in professional sports history to end the team’s drought — surely such lightning couldn’t strike twice, could it?It could, and did. On Wednesday night, Epstein’s Cubs did what previously had been reserved for the realm of fantasy, bringing a World Series to Chicago’s North Side for the first time in 108 years. So, having pulled off the feat twice now, how do Epstein’s two curse-breaking teams stack up?First things first: The 2016 Cubs were probably better than the 2004 Red Sox. Although the Cubs had a penchant for doing things the hard way during the playoffs, they also had one of the best couple-dozen regular seasons in MLB history. By wins above replacement (WAR),1All mentions of WAR in this story will refer to an average between the competing versions offered at Baseball-Reference.com and FanGraphs.com. Chicago was the seventh-best World Series winner ever; Boston ranked 41st out of the 112 all-time winners. The Cubs also just edged out the Sox, according to FiveThirtyEight’s Elo team ratings,2Using the more complete version that’s adjusted for the quality of a team’s starting rotation. ranking 29th among World Series winners versus Boston’s 32nd-place finish. (To be fair, by another measure of Elo the 2016 Cubs ranked as the 70th-best team ever, slightly behind the 64th-ranked 2004 Red Sox.)But more interesting than straight rankings is the contrast in how each team was built. The 2004 Red Sox were a veteran team, the fourth-oldest World Series winner in history.3Using an average for the team’s regular-season roster that weights according to how much each player contributed to the team’s overall record as determined by WAR. They had old hitters — 22nd-oldest among historical champs, as weighted by each player’s regular-season plate appearances — and positively ancient pitchers — No. 1 all time, in fact, weighted by regular-season innings pitched. Epstein was handed a team full of vets when he took over as Boston’s general manager after the 2002 season, and he doubled down further by adding the likes of Curt Schilling (age 37 in 2004), Keith Foulke (31), Kevin Millar (32), Bill Mueller (33) and Mike Timlin (38) via trades or free agency.Epstein’s Cubs, on the other hand, were pretty average as far as the age of championship rosters go: They ranked 52nd-youngest out of the World Series’s 112 winners. But they also had an interesting split between the average ages of their lineup and their pitching staff. In keeping with the tradition of the 2004 Red Sox, Epstein once again assembled a pretty old group of pitchers in Chicago — eighth-oldest among all champs (though a full year-and-a-half younger than Boston’s grizzled staff in ‘04). Chicago’s position players, however, ranked 11th-youngest in championship history. The mix between fresh-faced kids such as Kris Bryant (age 24) and Anthony Rizzo (26) on the hitting side and aging pitchers such as Jon Lester (32), Jake Arrieta (30) and John Lackey (37) built the foundation for one of the most interestingly constructed rosters of any champion. Much of that difference came down to defense: Those Red Sox ranked sixth-to-last in baseball by defensive runs saved in 2004, instead typifying the classic mashing-over-fielding profile carried by many of that era’s sabermetric darlings. The defensive-minded Cubs, by contrast, illustrated the evolution of today’s data-driven teams, ranking first in baseball (by a wide margin) in DRS this season.Those kinds of distinctions particularly help put Epstein’s accomplishment in perspective. As one of the first wave of young, Ivy League-educated, statistically savvy general managers, Epstein was able to reverse Boston’s curse by building what was effectively the prototypical early-sabermetric ballclub: patience and power at the plate, and power pitching on the mound. If the ball was ever put in play, you took your chances with the most adequate defense you could cobble together while still propping up your on-base percentage and slugging average. The 2004 Red Sox were one of the first teams to win with that formula, but Epstein’s 2016 champion Cubs show how much the winning equation has changed as sabermetrics has matured. Now, the value of dynamic free-swingers like Javier Baez has been rediscovered, as has the importance of defense. The secret to breaking Chicago’s curse was very different than the one that broke Boston’s hex 12 years earlier.And if Epstein ever molds another champion elsewhere, it’s a good bet that team will look different than either the ‘04 Sox or the ‘16 Cubs. Another good bet: It will probably set another prototype for subsequent teams to follow, whether they’re trying to end a championship drought or not.
Co-offensive coordinator and quarterbacks coach Ryan Day speaks to the media on March 21. Credit: Jacob Myers | Assistant Sports EditorOhio State lost long-time college coaches Ed Warinner and Luke Fickell after the 2016-17 season. While some fans cheered the departure of Warinner and wished Fickell well in Cincinnati, the pedigree of OSU coaches got a whole lot more impressive with the addition of Ryan Day and Bill Davis.While Ryan Day will help guide redshirt senior J.T. Barrett through his last year in Columbus, Bill Davis will be tasked with leading the linebacker unit, arguably the pride and joy of the last few Buckeye football teams. Also taking on co-offensive coordinator duties with newly hired Kevin Wilson, Day most likely will be the coach who is most closely observed by fans after the OSU passing game struggled for the second straight season. Still, his time spent as the quarterbacks coach under coach Chip Kelly with both the Philadelphia Eagles and the San Francisco 49ers last season should help.Just three days into practice, Day sees a close similarity between OSU and NFL programs.“Real close,” he said on Tuesday. “First off, because the guys who are running around this field are like NFL players. From the skill guys to the guys up front, the guys here have done an unbelievable job recruiting. So, the talent level here is just like a lot of the NFL teams. And that’s what’s most impressive when you get out here for the first three days.”While Day might feel at home, he still faces a tough task in morphing Barrett back into the passer he was during his redshirt freshman season, when he threw for 34 touchdowns and completed 64.7 percent of his passes. Day sees the potential in Barrett to return to the form that pushed him into Heisman consideration, and is using his NFL experience to help the Scarlet and Gray signal caller.“I think that I was lucky enough to coach those guys for the last couple years in the NFL and focus on quarterback play and fundamentals,” Day said. “I really impart that to him every day and just kind of relaying some of that information to him. I think he really appreciates that. But he’s also been coached at a high level to this point too, so it’s just really building upon it at this point.”Day was an offensive coordinator in 2013 and 2014 with Boston College. During those seasons, the Eagles averaged 27.7 and 26.2 points per game.Linebackers coach Bill Davis speaks to the media on March 21. Credit: Jacob Myers | Assistant Sports EditorDavis brings in more “next level” coaching experience than Day, most notably as a coach under NFL defensive masterminds Bill Cowher, Dick LeBeau, Wade Phillips, Marvin Lewis and Dom Capers. Entering his 26th season of coaching, Davis has belonged to coaching staffs of nine different NFL teams.Players have been feeling the differences in the way he coaches, and compare it to an authentic, NFL style.“Definitely. The first day, the first meeting, you could tell,” junior linebacker Jerome Baker said. “This has to be a NFL meeting room because, the way he coach(es), his style especially, is geared toward pro athletes. You could tell that he’d been in the NFL for a few years.”Davis has coached numerous notable NFL linebackers, such as D’Qwell Jackson, Connor Barwin and Kevin Greene. Like Day, Davis also sees similarities in the OSU program and the NFL.“As much as it can be,” Davis said. “The difference is the classes that the young men have to go to, we don’t have in the NFL. So the structure of the work is a little bit different. But what separates the Ohio State guys is the total growing of the man. I really am in awe of how coach Meyer and his staff and the system grows a human being, not just a football player. So what we found in the NFL is when Ohio State guys come, their mental toughness, because they go through this system of the grind, of hard, they come in so mentally tough, it’s tough to trip up Ohio State guys. So that’s why you see the young guys succeeding in the NFL. Because the talent level is the same.”There is still plenty of time for things to change, but the overall impression the new coaches have made on the staff has been positive. The impact of Day and Davis will be on display on April 15 in Ohio Stadium during the spring game, with kickoff scheduled for 12:30 p.m.