PHILADELPHIA, Pennsylvania:Jamaica had to play second fiddle to the United States in the Nike USA versus The World events on yesterday’s final day of competition at the 2016 Penn Relays.Jamaica’s only success came in the men’s 4×100 metres, where they took advantage of a big mistake by a strong USA Red team to win the event in 39.70 seconds. Jermaine Hamilton, Julian Forte, Rasheed Dwyer and Oshane Bailey were the members of the winning Jamaica quartet. USA Blue placed second in 39.02, with third going to St Kitts in 39.49. The USA Red team included the likes of Justin Gatlin, Tyson Gay and Mike Rodgers.Jamaica were second in three women’s relays the 4x100m in 42.90, 4x200m in 1:31.34, and 4×400 m in 3:29.00. The Americans won five of six relays contested.- R.G
India A suffered a two wicket loss at the hands of hosts South Africa A in a low-scoring one-dayer of the cricket Tri-Series here on Wednesday.Put in to bat, India A recovered from a batting collapse, thanks to captain Manish Pandey’s patient 95-ball 55 and cameos from Karun Nair (25) and Yuzvendra Chahal (24 not out), but were skittled out for 152.In reply, South Africa A stumbled on their way losing eight wickets before overhauling the target in 37.4 overs.The hosts found themselves struggling at 16/2 after losing Henry Davids (5) and Dane Paterson (11) early. By the 16th over, the hosts had lost half their side with 71 runs on board as Reeza Hendricks (11), captain Khaya Zondo (15) and Heinrich Klaasen (24) were back in the dressing room.Thereafter, leg-spinner Yuzvendra Chahal’s twin strikes in the 32nd over got rid of Dwaine Pretorius (38) and J.T. Smuts (0) within a space of four balls.The chase looked tough for the hosts and it was only after Farhaan Behardien (37) and A.M. Phangiso (3) got together that the hosts sneaked a win with 12.2 overs to spare.Earlier, left-arm spinner Aaron Phangiso returned with a four-wicket haul and Pretorius scalped three to skittle out India A for a low total.Brief Scores: India A 152 (Manish Pandey 55, Karun Nair 25, Yuzvendra Chahal 24 not out, Aaron Phangiso 4/30, Dwaine Pretorius 3/24) lose to South Africa A 153/8 (Dwaine Pretorius 38, Farhaan Behardien 37, Yuzvendra Chahal 3/41, Axar Patel 2/35) by 2 wickets.
CaliforniaSouth4187186.54.00.7 IndianaEast51938126.96.36.199 BaylorWest5183785.56.01.0 DaytonMidwest71788188.8.131.52 Florida Gulf CoastEast16154471.4<0.1<0.1 North CarolinaEast1207593.943.615.0 VirginiaMidwest1205292.530.49.8 Note, however, that Elo is still just one of six computer rankings that we use for the men’s tournament. The other five are ESPN’s BPI, Jeff Sagarin’s “predictor” ratings, Ken Pomeroy’s ratings, Joel Sokol’s LRMC ratings, and Sonny Moore’s computer power ratings. In addition, we use two human-generated rating systems: the selection committee’s 68-team “S-Curve”, and a composite of preseason ratings from coaches and media polls. The eight systems — six computer-generated and two human-generated — are weighted equally in coming up with a team’s overall rating.We’ve calculated Elo ratings for men’s teams only. For women’s ratings, we rely on the same composite of ratings systems that we used last year. You can find more about the methodology for our women’s forecasts here.As has been the case previously, our ratings are also adjusted for travel distance and (for men’s teams only) player injuries. Our injury adjustment has been slightly improved to account for the higher or lower caliber of replacement players on different teams: Stony Brook, for example, won’t be able to replace a star player as easily as Kentucky can.As a final reminder, these forecasts are probabilistic — something especially important to consider in the men’s tournament this year when there’s about as much parity among teams as we’ve ever seen. In some sense, every team but the UConn women should be thought of as underdogs to win the tournament this year.Check out FiveThirtyEight’s 2016 March Madness Predictions. Michigan StateMidwest2207891.833.98.9 VillanovaSouth2204591.322.46.4 Weber StateEast15162373.3<0.1<0.1 North Carolina-AshevilleSouth15155374.2<0.1<0.1 ConnecticutSouth91872184.108.40.206 Middle TennesseeMidwest15163875.0<0.1<0.1 SouthernWest16139268.0<0.1<0.1 DukeWest4191087.312.11.7 XavierEast21973220.127.116.11 IowaSouth7190418.104.22.168 Arkansas-Little RockMidwest12173478.90.2<0.1 Stephen F. AustinEast14182481.00.4<0.1 KentuckyEast4201490.715.94.4 OregonWest1203388.022.62.6 KansasSouth1209794.545.1%19.1% TexasWest6178822.214.171.124 UtahMidwest31887126.96.36.199 Miami (FL)South31933188.8.131.52 Holy CrossWest16142066.9<0.1<0.1 Texas A&MWest3191586.812.42.4 VanderbiltSouth111846184.108.40.206 MarylandSouth51876220.127.116.11 Fresno StateMidwest14170876.6<0.1<0.1 West VirginiaEast3195689.316.23.4 2016 NCAA Tournament team ratings Wichita StateSouth11189318.104.22.168 Stony BrookEast13166377.10.1<0.1 CincinnatiWest9179422.214.171.124 WisconsinEast71896126.96.36.199 OklahomaWest2197290.032.06.8 HawaiiSouth13173778.0<0.1<0.1 PittsburghEast101787188.8.131.52 Saint Joseph’sWest81814184.108.40.206 ColoradoSouth8175681.50.4<0.1 Northern IowaWest11175180.20.8<0.1 Welcome to FiveThirtyEight’s forecasts of the men’s and women’s NCAA basketball tournaments. We’ve been issuing probabilistic March Madness forecasts in some form since 2011, when FiveThirtyEight was just a couple of us writing for The New York Times. While the basics of the system remain the same, we unveil a couple of new wrinkles each year.Last season, we issued forecasts of the women’s tournament for the first time. Our big change for this year is that we won’t just be updating our forecasts at the end of each game — but also in real time. If a No. 2 seed is losing to a No. 15 seed, you’ll be able to see how that could affect the rest of the bracket, even before the game is over.Live win probabilitiesOur interactive graphic will include a dashboard that shows the score and time remaining in every game as it’s played, as well as the chance that each team will win that game. These probabilities are derived using logistic regression analysis, which lets us plug the current state of a game into a model to produce the probability that either team wins the game. Specifically, we used play-by-play data from the past five seasons of Division I NCAA basketball to fit a model that incorporates:Time remaining in the gameScore differencePre-game win probabilitiesWhich team has possession, with a special adjustment if the team is shooting free throws.These in-game win probabilities won’t account for everything. If a key player has fouled out of a game, for example, his or her team’s win probability is probably a bit lower than we’ve listed. There are also a few places where the model experiences momentary uncertainty: In the handful of seconds between the moment when a player is fouled and the free throws that follow, we use the team’s average free-throw percentage. Still, these probabilities ought to do a reasonably good job of showing which games are competitive and which are in the bag.We built a separate in-game probability model for the women’s tournament that works in exactly the same way but uses historical women’s data. Thus, we’ll be updating our forecasts live for both the men’s and women’s tournament.Excitement indexOur March Madness “excitement index” (loosely based on Brian Burke’s NFL work) is a measure of how much each team’s chances of winning changed over the course of the game and is a good reference for picking the best games to flip to.The calculation is simple: It’s the average change in win probability per basket scored, weighted by the amount of time remaining in the game. This means that a late-game basket has more influence on a game’s rating than a basket near the beginning of the game. We give additional weight to changes in win probability in overtime. Ratings range from 0 to 10, except in extreme cases where they can exceed 10.The index isn’t perfect — this year’s play-in game between Holy Cross and Southern was good, but perhaps not deserving of its 9.4 rating. But even if it doesn’t quite capture the difference between a closely contested slog and a Dunk City run to the Sweet 16, it does a nice job of quantifying how tight a game was and how many big shots were hit.Elo ratingsOtherwise, the methodology for our men’s forecasts is also largely the same as last year. But we’ve developed our own computer rating system — Elo — which we include along with the five computer rankings and two human rankings we used previously.If you’ve followed FiveThirtyEight, you’ll know that we’re big fans of Elo ratings, which we’ve introduced for the NBA, the NFL and other sports. We’ve now applied them for men’s college basketball teams dating back to the 1950s, using game data from ESPN, Sports-Reference.com and other sources.Our methodology for calculating these Elo ratings is highly similar to the one we use for NBA. They rely on relatively simple information — specifically, the final score, home-court advantage, and the location of each game. (College basketball teams perform significantly worse when they travel a long distance to play a game.) They also account for a team’s conference — at the beginning of each season, a team’s Elo rating is regressed toward the mean of other schools in its conference — and whether the game was an NCAA Tournament game. We’ve found that historically, there are actually fewer upsets in the NCAA Tournament than you’d expect from the difference in teams’ Elo ratings, perhaps because the games are played under better and fairer conditions in the tournament than in the regular season. Our Elo ratings account for this and also weight tournament games slightly higher than regular season ones.Elo ratings for the 68 teams to qualify for the men’s tournament follow below. North Carolina-WilmingtonWest13172277.70.2<0.1 YaleWest12179280.21.0<0.1 Cal State BakersfieldWest15163575.00.1<0.1 GonzagaMidwest11191686.03.20.5 TEAMREGIONSEEDELOCOMPOSITEFINAL 4CHAMPS Southern CaliforniaEast8173381.40.2<0.1 IonaMidwest13175978.20.1<0.1 Texas TechMidwest8177781.30.4<0.1 TempleSouth10173078.50.2<0.1 ArizonaSouth6195389.06.01.8 Oregon StateWest7174077.60.2<0.1 Seton HallMidwest61914220.127.116.11 Virginia CommonwealthWest10179818.104.22.168 Fairleigh DickinsonEast16141766.7<0.1<0.1 ChattanoogaEast12161076.6<0.1<0.1 South Dakota StateSouth12173578.60.2<0.1 ButlerMidwest9181522.214.171.124 MichiganEast11176879.60.3<0.1 BuffaloSouth14161375.7<0.1<0.1 PurdueMidwest5193888.713.02.7 Iowa StateMidwest41867126.96.36.199 Austin PeaySouth16147768.8<0.1<0.1 RATINGSPROBABILITY OF… HamptonMidwest16148868.6<0.1<0.1 SyracuseMidwest101772188.8.131.52 ProvidenceEast91824184.108.40.206 TulsaEast11169079.90.2<0.1 Notre DameEast61832220.127.116.11 Green BayWest14166776.20.1<0.1 UPDATE (6:30 p.m. March 18): We’ve updated this post to add information about the excitement index.
The man behind the bomb attack on Borussia Dortmund’s bus in 2017 has been sentenced to 14 years in prisonThe defendant, who only goes by the name of Sergej W. due to German privacy laws, has been convicted on 28 accounts of attempted murder.The Daily Echo reports that the sentence was handed out in a Dortmund state court today.The attack occurred on April 11, 2017, as the Dortmund team travelled to the Signal Iduna Park for a Champions League quarter-final match with AS Monaco.Merson believes Arsenal should sign Sancho Manuel R. Medina – September 14, 2019 Borussia Dortmund winger Jadon Sancho might be the perfect player to play for the Gunners, according to former England international Paul Merson.Defender Marc Bartra, now at Real Betis, and a police officer were both injured as three explosions were set off near the bus.Sergej W allegedly betted that Dortmund’s shares would drop in value by taking out a loan.He later launched a bomb attack on the team bus and tried to cover it up as Islamic terrorism.