‘THREE!’ chronicles the Warriors run to the 2018 NBA Championship.Order the book today!SAN FRANCISCO – Usually, nothing bothers Klay Thompson.He remains content with winning and his role. He has reiterated his hopes to remain with the Warriors for the rest of his NBA career. He has enjoyed a busy albeit relaxing offseason with promotional clinics in China, a basketball camp in the Bahamas, a visit to Charlotte, N.C to see younger brother, Trayce, play for the Chicago White Sox. He also has …
22 September 2011 South Africa’s reputation among people of the G8 countries has shown a steady improvement since 2009, the Reputation Institute said on Wednesday, with recent gains in beating crime boding well for further improvement still. The global reputation consulting firm said South Africa’s reputation had shown a steady year-on-year improvement since 2009, from a score of 44.27 in January 2009, to 44.60 in January 2010, and 46.70 in January 2011 (on a score scale of 0-100). South Africa’s reputation score spiked at 49.11 in August 2010, after the Fifa World Cup held at venues around the country. The G8 comprises the world’s eight most industrialised nations: Canada, Germany, Japan, Italy, France, Russia, the UK and the US. The institute said the top driver of South Africa’s reputation among people of the G8 countries in 2011 was whether people were welcoming and friendly. The perception of whether the country was a safe place was the second most important of the 16 drivers measured in 2011. South Africa achieved a “weak” score of 57.84 in the top driver, and its score as a safe place was considered poor at 37.32. Perceptions of the government’s effectiveness achieved a “weak” 41.6. The country scored the highest for physical beauty (72.19) and enjoyment (71.21), but these were only the fifth and seventh most important drivers of South Africa’s reputation. “This indicates that we can improve our reputation by working hard on safety and effective government,” the institute’s managing director in South Africa, Dominik Heil, said. “Recent gains that have been announced in safety and security are therefore really important and bode well for us in building a stronger reputation.” Out of the 50 countries measured in 2011, South Africa continued to be associated with mid-scale reputation countries such as Puerto Rico, South Korea, Mexico, Turkey and Egypt, and this year was ranked 33rd overall in the world. Several new countries were included in the 2011 survey, including Egypt and Nigeria. The survey ranked Egypt at 37th position. Nigeria was 47th, ahead only of Pakistan, Iran and Iraq. Canada was the best-regarded country in the world in 2011, while Sweden, Australia, Switzerland and New Zealand were in the next four. Norway, Denmark, Finland, Austria and Netherlands made up the rest of the top 10 countries in the world. Sapa
Australian Senior Teams Training Camp – preparations for the All Nations Championships
Last week, prompted by ESPN’s new “30 for 30” documentary on the “Bad Boys” Detroit Pistons, I examined the question of just how “bad” the Bad Boys really were. In that piece, I used relative technical foul rates as a proxy for “badness” to establish that the Bad Boys Pistons teams did, indeed, deserve that moniker. Their two championship squads were two of the “baddest” teams in the past few decades, earning more technical fouls relative to their peers than any other teams since 1982. But one question lingers: Were they so good because they were so bad, or in spite of it?To find out, I looked at 30 years’ worth of the league’s correlation between technical fouls and winning. Technicals are the NBA’s official in-game punishment for conduct that the league and officials deem “unsportsmanlike” (short of a flagrant foul), which is why we’re using it as our proxy for badness.1In the Bad Boys Era, what are now flagrant fouls were mostly just technical fouls, and didn’t carry the extra penalty they do today. They, of course, have the immediate and measurable result of giving the other team one free throw by the shooter of its choice — worth around .85 points on average.2There’s also a minor effect of sometimes adding time to the opponent’s shot clock.Despite that negative consequence, teams that get more technical fouls than average tend to be pretty good. What’s more, the more technicals they earn, the more likely they are to be even better.Here’s a plot of the number of technical fouls (badness) a team had relative to the league average that year against its win percentage (goodness). The data below is pulled from all team seasons since 1982-83,3Limited to teams for which we have at least 10,000 combined minutes worth of data. showing only those that were badder than average.Look at the red dots, which are rolling 25-team averages. As the teams get more techs — or get badder — their winning percentages increase. That’s intriguing, as is the fact that the top 26 baddest teams in the data set all had winning records. Overall, 63 percent of these bad teams were good enough to have a winning record, and the top 100 of them had an average winning percentage of 60.3 percent.4The correlation between technical rate and win percentage is .27, which is pretty high for any metric based on only one stat.But finding a relationship in one season isn’t enough. The real test is whether the metric predicts performance in other seasons.5This is called taking your test “out of sample,” which separates cause and effect. Note, though, that it doesn’t necessarily tell you which is which. Below you’ll find a graph showing how technical fouls predict team strength in neighboring seasons, and how they compare to a variety of other popular metrics. For strength, we’ll use SRS, or “Simple Rating System,” which is a team’s average margin of victory adjusted for strength of schedule6The technicals per game metric I used is calculated relative to each season, while the other metrics are not. This gives it a slight advantage.:Effective field goal percentage comes out on top of this group, but technical foul rate holds its own, coming out as a better predictor of past or future team strength than stats stalwarts like points per game or rebounding percentage.7Also, technicals are more positively predictive than turnovers are negatively predictive, which is fascinating but beyond the scope of this article.That’s a bit wacky — the technical foul, remember, can’t provide value directly, because it gives up .85 points (on average) to the opposition. From where I sit, then, there are two potential kinds of explanation: Explanations that embrace the nasty. These would argue that teams that get more technical fouls are better because the behavior that leads to the technicals (i.e., bad behavior) likely provides more benefit than the occasional .85 points that it costs.8OK, actually there’s a third line of thinking, which is that technical fouls don’t cost the .85 points that we think they do because, say, referees overcompensate for calling technicals by giving teams better calls later in the game. But for all intents and purposes, I’ll treat those as part of the second theory. In baseball, high/inside pitches used to brush batters off the plate usually result in balls or sometimes even hit batters, but are commonly believed to be worth it (whether they actually are or not, I don’t know). For what it’s worth, I checked a boatload of possible confounding variables and combinations thereof, such as home/away (53 percent of technicals go to the away team); ahead/behind (57 percent go to the trailing team); and playoffs/regular season (if it were strictly a matter of effort, we would expect a difference when all teams have equal incentives to play hard. No major differences found). Coaching technicals appear to be at least as predictive as player technicals. If there’s a correlation between aggressive play and winning and aggressive coaching and winning, Occam’s Razor suggests that you should favor a single theory that explains both phenomena, such as that an aggressive ethos (which applies equally to coaches and their players) causes winning. In football, I’ve found that rookie quarterbacks who throw more interceptions (all else being equal) often have more productive careers. In basketball, offensive rebounds have a potentially similar problem from the opposite direction: While apparently a good thing, in quantity they signal that a team doesn’t shoot very well. In poker, a too-high showdown win percentage likely indicates that a player doesn’t bluff enough and/or doesn’t make enough marginal calls. So far my research hasn’t turned up any smoking gun proving the case one way or the other, but on balance I’d say the results are more consistent with the second option: Technical fouls exist to deter certain types of unsportsmanlike behavior, but if those behaviors are broadly advantageous (by intimidating or hurting the opponent, for example), they could be “priced incorrectly” at only (roughly) -.85 points each.9Compare it to the deterrence problem: In order to coerce different behavior, things have to be punished at a rate much worse than their actual effect.That something ostensibly negative can ultimately be predictive of something positive (or vice versa) isn’t an unheard of dynamic in sports. For instance: Not all good teams get a lot of technical fouls (the San Antonio Spurs, for example, consistently rank near the bottom of the league), but the vast majority of teams that get a lot of technical fouls are good. Of the 27 teams with the best winning percentages since 1982, two-thirds (18) have had more technical fouls than the league average at the time. (Compare that to the top 26 technical-getting teams having winning records.) But it’s unusual in basketball for an event with a negative impact to have a positive correlation with team strength. Take a look at some other things that have a direct impact on the game that’s similar to that of technical fouls (slightly above or below -1 point each):If everything else were equal, we would probably expect technicals to be in the same range as turnovers or steals, so the total gap from where they ought to be based on in-game value and where they actually are, predictively, is massive.10Note the gap between opponent offensive and defensive rebounds is smaller, even though there’s a straightforward reason that offensive rebounds are a mixed blessing (because it means the team is missing more shots).But even if we’re satisfied that technicals can predict wins, there’s still something we haven’t considered yet: Wins may predict technicals.11It’s like the Euthyphro question, but for sports gods: Are technicals good because the sports gods love them, or do the sports gods love technicals because they’re good? This theory has a few possible scenarios associated with it, such as: Teams that are in contention are playing hard all the time — so hard that they occasionally earn a technical — while teams that are out of contention don’t really care enough to do “whatever it takes” to win.That kind of explanation is intuitively appealing, both because the scenario has a plausible ring to it and because it’s the sort of unsexy answer you often find when you try to explain a strange result.To test this theory, I looked at play-by-play data over the last four years, which breaks fouls — including technicals — down by type. That yielded 1,963 player techs, 422 coach techs, 278 flagrants (similar to the technical, but with a much harsher punishment), and 2,448 three-second violations.12For the data set I used below, I also applied a number of filters: I filtered out the fourth quarter because variance is too great and tactical considerations trump other things. I also dropped hanging, taunting, non-unsportsmanlike and team technical fouls because their numbers are too small to break out, and I’d like to keep the main-line group as homogenous as possible.I combined all that with in-game win percentage calculations provided by Dean Oliver of ESPN Stats & Info, estimating the foul-committing team’s chances of winning before and after the foul (including the resulting free-throw).13I also duplicated all of this research using margin of victory so as not to rely entirely on the predictive algorithm, and the results were virtually identical. We’re interested in the difference between what that foul did to a team’s projected results and its actual results.Averaging across all plays, we can represent the results of this comparison in a slope chart that shows how the team’s chances should have changed in that moment, and how often it actually ended up winning. Take note of those two (well, four) lines for player and coach techs. Both player and coach technicals ostensibly cost teams about a 1.8 percent chance of winning the game, which is what we would expect based on the surrendered free throw. But the actual win percentages of technical-foul-getting teams appear much higher than we would expect. Teams ended up winning 2.1 percent more often than expected after player techs, and 3.8 percent more often than expected after coach techs.14Flagrant fouls don’t do as well, though they include a harsher penalty, including the possibility of the player being ejected.While this result supports our finding that technical fouls predict winning over an even larger number of observations, it’s also consistent with either type of explanation for why this is so. If there were any bias in how technical fouls are distributed — as suggested by the “wins predict technicals” theory — unfortunately it would still bias these results.But there’s something we can do to avoid that. Instead of computing the averages in that chart across every single foul, we can compute them on a team-by-team basis first, and then average the result across all teams equally — treating each team’s results as one data point regardless of how many technical fouls it received. That helps us avoid potentially skewed data if different types of teams (like winning teams) are more likely to get technicals in the first place. When we do that, here’s what we get (the new chart is on the right, with the old one on the left for comparison’s sake):Lo and behold, they’re extremely similar! Teams tend to win 1.4 percent more often when their players get a tech, and a whopping 5.5 percent more often when their coaches do. That similarity broadly suggests that “bad” (technicals) begets “good” (winning), rather than the other way around.To illustrate: If one great team, let’s call it SuperBad, earned every technical foul every year, but by virtue of being a great team won 5 percent more often than its expected win percentage would suggest, that would show up as a 5 percent gain in the chart on the left. (That’s because each time a team got a technical it won 5 percent more often, even though it was the same team every time, and even if the winning was unrelated.) But when averaged across all 30 teams in the league, it would only show a 0.16 percent gain in the chart on the right (SuperBad team ran 5 percent above average when getting a technical, but the other 29 teams ran 0 percent better15OK, technically undefined in this example, so add epsilon if you must.). This would be a perfect “winning begets technicals” scenario.On the other hand, if every team got an equal share of the same number of technicals as our SuperBad team, and every time a team got a technical it won 5 percent more often than it would have otherwise, it would show up both as a 5 percent gain on the left and a 5 percent gain on the right. This would be a perfect “technicals beget winning” scenario.The charts above seem much much closer to this second “technicals beget winning” scenario, as there doesn’t appear to be much difference whether we aggregate by plays or by teams. Indeed, the main reason this isn’t a smoking gun is that the sample size for the right-hand chart is only 120 team seasons, which would normally be much too small to even attempt to draw conclusions about differences of only a couple of percentage points either way. But being so consistent with the much larger sample of the play-by-play chart is powerful corroboration.Here are a few other things that cut against the “winning predicts technicals” theory: Finally, let’s return to the question that kicked off the piece: Were the Bad Boys Pistons so good because they were so bad, or in spite of it?Based on what I’ve looked at so far, I’d say the former has the stronger case: While technical fouls can’t lead directly to winning, the types of behavior that lead to technical fouls just may. Explanations that avoid the nasty conclusion that unsportsmanlike play gives a team an advantage. For example, it could be that technical fouls are committed more often by teams that are already winning, or that winning teams and players just have a propensity to get more technical fouls, and are willing to absorb the cost.
In the eternally running discussion thread “Hey Bill” at billjamesonline.com, the website of sabermetric legend Bill James, the question came up of measuring the growth of sabermetric knowledge. James’s idea? Measure the extent to which teams are taking park factors into account when judging their rosters. But Tom Tango, author of “The Book,” offered another gauge: look at which teams are using good hitters in the No. 2 lineup slot.Traditionally, the two-hole was the domain of contact hitters with good bat control, with premiums placed on the ability to hit behind the runner, to sacrifice bunt, and to generally move the leadoff man over (even if it meant making an out). You can see this statistically: During Major League Baseball’s expansion era (1961-present), the No. 2 slot has the highest aggregate contact rate of any batting order position.But research by Tango and his compatriots suggests teams have been doing it wrong. After examining how important each batting event (single, double, walk, etc.) is to each lineup slot — based on factors such as how many runners are likely to be on base and how many outs they’re likely to hit with — the data says a team ought to bat its three best hitters in the No. 1, No. 2 and No. 4 slots, with the most balanced hitter occupying the two-hole. That’s a far cry from the conventional wisdom of slotting the best hitter either third or fourth, and putting a weak contact specialist at No. 2.So, if there are more good hitters in the second position, it’s a possible sign sabermetrics has penetrated the managerial mindset. But if there’s a pattern toward a more enlightened lineup card, it’s not detectable by looking at the average quality of No. 2 hitters (according to weighted runs created, known as wRC+) since the introduction of the designated hitter in 1973:If we take a five-year moving average to smooth out year-to-year variance above, it’s even clearer that we’re not in the golden age of great hitters batting second:Historically, the quality levels of MLB leadoff and No. 2 hitters tend to track with each other — and contra the performances of third and fourth hitters. (Meanwhile, Nos. 5 and 6 have stayed fairly stable over the years, with the five slot outproducing six by a decent amount.) The good news is that it appears the two-hole has emerged from the dark ages of the mid-1990s to the mid-2000s, when slot Nos. 3 and 4 vastly outpaced Nos. 1 and 2.It may not be coincidental that the bleakest of times for the No. 2 spot came during MLB’s so-called steroid era. The stat we’re using, wRC+, compares a player’s per-plate appearance productivity against the average of all hitters, and the power hitters who frequently bat third and fourth may have received the benefits of performance-enhancing drugs at a greater rate than the overall population of MLB batters. (This would cause No. 2 hitters to move backward relative to the overall average, even if they themselves saw no change in talent.) With the specter of performance-enhancing drugs reduced in today’s game, the gap between hitter No. 2 and Nos. 3 and 4 has returned to its long-term norm.Still, today’s two-hole batters lag behind those of the halcyon late 1980s and early 1990s, when players such as Ryne Sandberg, Tony Gwynn, Wade Boggs, Roberto Alomar, Julio Franco and Lou Whitaker were doing a large share of their damage from the second spot in the lineup. It’s plausible that the conditions of the game back then simply favored the traditional archetype of the No. 2 hitter more (batting averages were higher, as was the ratio of on-base percentage to slugging), but today’s managers also don’t appear to be moving toward the sabermetric ideal of penciling the team’s best hitter into the No. 2 spot.Sabermetrics has come a long way since the first analysts began tinkering with mathematical models, and there are certainly places where statistical thinking has made its way onto the field (for example, the explosion of defensive shifts in today’s game is rooted in probability theory regarding where a batter is most likely to hit the ball). But when it comes to the two-hole, baseball’s decision-makers still have a bit of a climb ahead of them.
Play ‘Em Matt Cassel (Kansas City): One quarterback not getting the respect he deserves is Cassel, who has thrown for 18 touchdowns versus four interceptions. Much of that production is because of Dwayne Bowe’s emergence as a solid wide receiver. Either way, Cassel has a nice matchup against Seattle, which ranks in the top 10 in fantasy points allowed to opposing quarterbacks. Mark Sanchez (New York): Last week, Sanchez, battling a calf injury, threw for 315 yards with three touchdowns and one interception against Houston. That performance gives Sanchez three straight weeks with at least 27 fantasy points in standard scoring leagues. Consider Sanchez a low-end No. 1 fantasy quarterback against a Bengals defense that got torched by the Bills’ Ryan Fitzpatrick to the tune of 316 yards and four touchdowns last week. Mike Tolbert (San Diego): Tolbert continued his breakout 2010 season with an impressive performance Monday night against Denver, with 111 rushing yards with a touchdown. Look for similar numbers against a Colts run defense that allows the sixth-most fantasy points to opposing running backs. Darren McFadden (Oakland): McFadden has been quiet the last two weeks, totaling 103 rushing yards with zero touchdowns. Granted, Week 11 was against Pittsburgh. McFadden should return to form against a Dolphins’ defense that allows 113 rushing yards per game. Based on the matchup, McFadden is a low-end No. 1 running back. Steve Johnson (Buffalo): Like Tolbert, Johnson is having a breakout 2010 season, with 728 receiving yards and nine touchdowns. Although Johnson faces the Steelers, he is a must-start from now on. The Steelers rank 22nd in pass defense and have allowed five passing touchdowns to opposing wide receivers in their past five games. So, Steve Johnson, “Why So Serious?” Vincent Jackson (San Diego): Jackson is back after serving a three-game suspension and signing his contract late. This is great timing because Patrick Crayton (wrist) is out, Antonio Gates is still battling a foot injury, and Malcom Floyd tweaked his hamstring. Jackson will benefit by having the league’s leading passer in Philip Rivers and going against a Colts defense allowing 208 passing yards per game. Bench ‘Em David Garrard (Jacksonville): Garrard looked terrible last week, with 254 yards, two touchdowns and three interceptions. The Jaguars’ offense will stall a little with Mike Sims-Walker out. This week, Garrard faces a Giants defense that allowing 14 fantasy points per week. Fred Jackson (Buffalo): Jackson has been solid in his past three games, with four touchdowns and back-to-back 100-yard games. This week, he faces a Steelers defense allowing 63 rushing yards per game and a total of four touchdowns on the ground. Only the Patriots’ BenJarvus Green-Ellis has managed to go over 50 rushing yards against the Steelers. Look for other options at the running back position this week. Beanie Wells (Arizona): Wells had eight carries for 39 yards last week against Kansas City. Wells’ knee continues to be a burden and the Cardinals continue to use a two-back system. In Week 12, Wells faces a Niners defense that ranks ninth in fantasy points allowed to opposing running backs. Brandon Marshall (Miami): It’s unclear if Marshall will play this week against Oakland (top five pass defense) because of a hamstring injury. Marshall hasn’t recorded double-digit fantasy points in standard leagues since Week 6. The Dolphins’ offense is a mess, led by quarterback Tyler Thigpen. Keep Marshall on your bench and hope he’s ready for the playoffs. Johnny Knox (Chicago): Knox continues to be Cutler’s favorite target, with five catches for 55 yards on eight targets last week. But the attention hasn’t translated to fantasy production. The yards are there but the touchdowns are not (one for the year). Expect corner Asante Samuel, who leads the NFL with seven interceptions, to be defending Knox, who is more of a flex option against the Eagles.
Dan Cohen AUTHOR The Federal Aviation Administration has selected Brunswick Executive Airport, Brunswick, Maine to participate in the fiscal 2016 Military Airport Program (MAP), allowing the business and general aviation airport at the former Brunswick Naval Air Station to remain in the program for five additional years.Brunswick was the only former military airport or joint use airport added to MAP this year, the third straight year FAA has picked only one participant. A total of 15 airports can participate in the program at one time.Brunswick’s selection will allow it to complete projects that started during the previous five years, according to an FAA fact sheet. Those projects include converting military hangars to civilian use, obstruction removal, drainage upgrades and installing wildlife fencing.MAP provides a critical source of federal funding for capital needs to support joint use airports and to convert former military airports to civilian use. MAP, a set-aside of the Airport Improvement Program (AIP), covers projects such as building or rehabilitating parking lots, fuel farms, hangars, utility systems, access roads, cargo buildings and other airfield projects. Many of these projects are not normally eligible for AIP funding, but projects for MAP-designated airports have unique eligibility rules to convert them to civilian or joint use.Airports already participating in MAP include:Kaleaeloa/John Rodgers Field, Kapolei, HawaiiCastle Airport, Atwater, Calif.Northwest Florida Regional Airport at Eglin AFB, Valparaiso, Fla.Griffiss International Airport, Rome, N.Y.Alexandria International Airport (former England AFB), Alexandria, La.José Aponte de la Torre Airport (former Naval Station Roosevelt Roads), Ceiba, Puerto Rico