Dreher finished the first round at two over par and knew she was near the top of the leaderboard, and a round of one over on the second round helped to solidify her spot.Accuracy off the tee was paramount as course conditions weren’t ideal, but Dreher says she was able to keep the ball in the fairways throughout the day.“It was cold and rainy over the first 18 holes. The course was super tough and you had to hit a lot of fairways because they had the roughs so thick that if you hit it there, you couldn’t go for the green,” she explains. “That was probably the thing I did the best out there yesterday was that I hit a ton of fairways. My driver was definitely my best club in the bag.”- Advertisement -Dreher says her game plan worked perfectly, as she kept a level head throughout the round.“My game plan was to stick to my routine and let whatever happens, happens,” she says. “36 holes is a ton of golf for everybody and so much can happen, even over nine holes, so whatever I was at, at that present moment I had no clue what anyone else was doing, so I just had to stick to my routine and keep plugging along.”This year’s U.S. Women’s Open will be held in South Hampton, New York. The tournament will tee off June 27 and will conclude on the 30th.Advertisement
As he took the Mall of Asia Arena floor for the do-or-die clash with Letran on coach Egay Macaraya’s birthday, San Sebastian stalwart Michael Calisaan thought about the time when his mentor had not given up on him despite a motorcycle accident more than a year ago that nearly took his life.For Calisaan, there was no better time to repay the faith shown to him by Macaraya over the past year with the Stags’ season on the line and a first Final Four appearance in four years within reach.ADVERTISEMENT Read Next Batang Pinoy: Jabol captures long jump, 100-m dash titles just minutes apart Japan ex-PM Nakasone who boosted ties with US dies at 101 Don’t miss out on the latest news and information. Kin of Misamis Oriental hero cop to get death benefits, award — PNP Costelo also shone in the final quarter, pouring nine of his 13 points for the Stags, who will clash with third seed Jose Rizal U Heavy Bombers in the next phase of the stepladder semifinals on Friday. The survivor of the duel will go up against second seed San Beda with the winner advancing to the finals against unbeaten Lyceum.“I think we deserve to be in the Final Four,” said Macaraya.The Knights tied the game for the last time at 69 before Costelo hit a teardrop with 44 seconds remaining.“One of our concerns this season is that we don’t have an import and we didn’t have the ceiling,” said Macaraya. “But the team showed heart, the character and desire to win and to reach the Final Four.”ADVERTISEMENT Stronger peso trims PH debt value to P7.9 trillion Kammuri turning to super typhoon less likely but possible — Pagasa CPP denies ‘Ka Diego’ arrest caused ‘mass panic’ among S. Tagalog NPA “I wanted to win it for coach,” said Calisaan in Filipino. “He took me in and gave me a chance despite my accident last year.”The 6-foot-4 Calisaan gave Macaraya a fitting gift for his 56th birthday as he fired a career-best 36 points and grabbed 10 rebounds to power the Stags to a 74-69 victory over the Knights that sent San Sebastian to the next round of the stepladder semifinals of NCAA Season 93.FEATURED STORIESSPORTSWATCH: Drones light up sky in final leg of SEA Games torch runSPORTSSEA Games: Philippines picks up 1st win in men’s water poloSPORTSMalditas save PH from shutout“I told him he had to make the most out of his second life,” said Macaraya, referring to Calisaan, who sustained near-fatal injuries due to a motorcycle accident in his hometown of Sta. Rita, Pampanga. “That accident was the turning point for him.”Calisaan nailed the biggest shot of the Stags’ season so far with a triple off a pass from Ryan Costelo with 18 seconds remaining, sealing the win after a nip-and-tuck final period. Typhoon Kammuri accelerates, gains strength en route to PH Trending Articles PLAY LIST 00:50Trending Articles01:53NCAA Season 93 Preview: San Sebastian Stags01:46US defense chief agrees it’s time to take another look at defense pact with PH01:37Protesters burn down Iran consulate in Najaf01:47Panelo casts doubts on Robredo’s drug war ‘discoveries’01:29Police teams find crossbows, bows in HK university01:35Panelo suggests discounted SEA Games tickets for students02:49Robredo: True leaders perform well despite having ‘uninspiring’ boss02:42PH underwater hockey team aims to make waves in SEA Games MOST READ Brace for potentially devastating typhoon approaching PH – NDRRMC QC cops nab robbery gang leader, cohort LATEST STORIES View comments
Model CreatorsNate Silver, Jay Boice and Neil Paine ReferencesBasketball-Reference.com / Real Plus-MinusElo ratings / Monte Carlo simulations / Box Plus/Minus / Simple Projection System The DetailsFiveThirtyEight’s NBA predictions have gone through quite an evolution over the years.Our first iteration simply relied on Elo ratings, the same old standby rating system we’ve used for college and pro football, college basketball, baseball, soccer, Formula One racing and probably some other sports I’m forgetting. Basic Elo is generally useful — and we still track it for teams going back throughout history — but it only knows who won each game, the margin of victory and where the game was played. So if a player is injured or traded — or resting, as is increasingly the case in the NBA — Elo wouldn’t be able to pick up on that when predicting games or know how to account for that in a team’s ratings going forward. In fact, even if a team simply made a big offseason splash (such as signing LeBron James or Kevin Durant), Elo would take a long time to figure that out, since it must infer a change in team talent from an uptick in on-court performance.To try to address that shortcoming, in 2015 we introduced a system we called “CARM-Elo.” This still used the Elo framework to handle game results, but it also used our CARMELO player projections to incorporate offseason transactions into the initial ratings for a given season. In a league like the NBA, where championships now feel like they’re won as much over the summer as during the season itself, this was an improvement. But it still had some real problems knowing which teams were actually in trouble heading into the playoffs and which ones were simply conserving energy for the games that matter. Since a team’s underlying talent is sometimes belied by its regular-season record — particularly in the case of a superteam — an Elo-based approach to updating ratings on a game-to-game basis can introduce more problems than it actually solves.Moving beyond EloOne attempt to salvage CARM-Elo was to apply a playoff experience adjustment for each team, acknowledging the NBA’s tendency for veteran-laden squads to play better in the postseason than we’d expect from their regular-season stats alone. This also helped some, but CARM-Elo still had problems with mega-talented clubs (such as the 2017-18 Golden State Warriors) that take their foot off the gas pedal late in the NBA’s long regular season. It was clear our prediction system needed a major overhaul, one that involved moving away from Elo almost completely.As we hinted at in our preview post for the 2018-19 season, we made some big changes to the way we predict the league that year. Chief among them is that our team ratings are now entirely based on our CARMELO player projections. Specifically, each team is judged according to the current level of talent on its roster and how much that talent is expected to play going forward. Here’s how each of those components work:Talent ratingsAt their core, our CARMELO projections forecast a player’s future by looking to the past, finding the most similar historical comparables and using their careers as a template for how a current player might fare over the rest of his playing days. After running a player through the similarity algorithm, CARMELO spits out offensive and defensive ratings for his next handful of seasons, which represent his expected influence on team efficiency (per 100 possessions) while he’s on the court. You can think of these as being similar to ESPN’s Real Plus-Minus or other adjusted plus-minus-style ratings.The player ratings are currently based on a blend between Real Plus-Minus (RPM), Box Plus/Minus (BPM) and, on defense, our new DRAYMOND metric. In our mind, each of these metrics represents a different facet of measuring player performance: BPM to capture and weight the player’s contributions in the traditional box score; RPM to measure the effect he has on his team’s efficiency when he is on the court; DRAYMOND to capture a critical but underutilized aspect of defense, which is to reduce opponent shooting percentages by contesting and otherwise disrupting shots. A blend of all applicable metrics appears in the CARMELO individual pages under the player’s offensive rating (using BPM and RPM) and defensive rating (using BPM, RPM and DRAYMOND).These blended ratings provide a prior for each player as he heads into the current season. But they must also be updated in-season based on a player’s performance level as the year goes on. To do that, we have two methods (for both offense and defense) depending on the data available:Using Real Plus-Minus and Box Plus/Minus. Ideally, both metrics will be published during a season, allowing us to use a blend (⅔ weight for RPM; ⅓ for BPM) on each side of the ball to update our prior ratings. When that happens, we assign a weight to the prior that is relative to 1 minute of current-season performance. On offense, that weight is calculated with a constant term of 416 minutes, plus 0.3 times a player’s minutes from the season before, plus 0.15 times his minutes from two seasons before, plus 0.05 times his minutes from three seasons before. That number is multiplied by his CARMELO preseason offensive rating and added to the product of his current-season minutes and current-season offensive plus/minus blend, and divided by the sum of current-season minutes and the prior weight to get an updated offensive rating. (The rating for players with 0 current-season minutes played is, by definition, the prior.) On defense, the weight has a constant of 60 minutes, plus 0.3 times a player’s minutes from the season before, plus 0.15 times his minutes from two seasons before, plus 0.05 times his minutes from three seasons before. This weight is combined with current-season performance in the same manner as on offense.Using Box Plus/Minus only. At a certain stage of each season, ESPN will not have released RPM data for the current season yet. During that time, we must update the in-season ratings using only BPM, which is usually available since the very start of the season via Basketball-Reference.com. Just like with our blended number from above, we assign a weight to the prior that is relative to 1 minute of current-season performance — but we must use different weights because BPM is not quite as reliable an indicator of player performance as RPM (or our RPM-BPM blend). On offense, the weight is calculated with a constant term of 703 minutes, plus 0.27 times a player’s minutes from the season before, plus 0.13 times his minutes from two seasons before, plus 0.04 times his minutes from three seasons before. That number is multiplied by his CARMELO preseason offensive rating and added to the product of his current-season minutes and current-season offensive plus/minus blend, and divided by the sum of the current-season minutes and the prior weight to get an updated offensive rating.On defense, the weight has a constant of 242 minutes, plus 0.48 times a player’s minutes from the season before, plus 0.24 times his minutes from two seasons before, plus 0.08 times his minutes from three seasons before. This weight is combined with current-season performance in the same manner as on offense.Regardless of the version being used, these talent ratings will update every day throughout the regular season and playoffs, gradually changing based on how a player performs during the season.Overnight updatesBecause our data sources for player ratings (ESPN and Basketball-Reference.com) don’t update individual statistics immediately after the end of every game, we added a function to preliminarily estimate the changes to a team’s rating as soon as a game ends. For each player in our database, we adjust his offensive and defensive ratings up or down very slightly after each game based on his team’s margin of victory relative to CARMELO’s expectation going into the game. These numbers add up at the team level to reflect how we predict that a team’s ratings will change in the wake of a given result.The advantage of this is that we can provide an instant update to the model as soon as a game ends. However, since these estimates are stopgaps, they will be changed to the RPM/BPM-based ratings from above when the data from those sources updates. After any given game, these differences should be small and generally barely noticeable. But we think this change will be particularly worthwhile in the playoffs, when team odds can shift dramatically based on a single game’s result.Playing-time projectionsNow that we have constantly updating player ratings, we also need a way to combine them at the team level based on how much court time each player is getting in the team’s rotation.For CARM-Elo’s preseason ratings, we used to accomplish this by manually estimating how many minutes each player would get at each position. Needless to say, this is a lot more work to do in-season (and it requires a lot of arbitrary guesswork). So as part of our move toward algorithmizing our predictions in a more granular way, we developed a program that turns simple inputs into a matrix of team minutes-per-game estimates, broken down by position.This system requires only a rank-ordered list of players on a given team by playing-time preference (the default order is sorted by expected rest-of-season wins above replacement), a list of eligible positions a player is allowed to play (the system will assign minutes at every player’s “primary” position or positions first, before cycling back through and giving minutes at any “secondary” positions when necessary to fill out the roster) and some minutes constraints based largely on CARMELO’s updating minutes-per-game projections.For that last part, we have developed an in-season playing-time projection similar to the one we use to update our individual offensive and defensive ratings. For each player, CARMELO will project a preseason MPG estimate based on his own history and the record of his similar comparables. We then adjust that during the season by applying a weight of 12.6 games to the preseason MPG projection, added to his current-season minutes and divided by 12.6 plus his current-season games played. (Interestingly, this implies that the amount of weight the MPG prior receives is the same regardless of whether the player is a fresh-faced rookie or a grizzled veteran.)Those minutes are used as the default for our program, which then automatically creates a team’s depth chart and assigns minutes by position according to its sorting algorithm. The defaults, however, can and will be tweaked by our staffers to help the program generate more accurate rosters. For instance, we can mark certain games in which a player is injured, resting, suspended or otherwise unavailable, which will tell the program to ignore that player in the team’s initial rank-ordered list of players before allocating minutes to everyone else. (We also have a method of penalizing a player’s talent ratings if he is forced to play significantly more MPG than his updated CARMELO projection recommends.) New for 2020, there is even a “load management” setting that allows certain stars to be listed under a program of reduced minutes during the regular season.Through this system, we will be able to account for most injuries, trades and other player movement throughout the season on a game-by-game basis.Because of the differences between a team’s talent at full strength and after accounting for injuries, we now list two separate CARMELO ratings on our interactive page: “Current CARMELO” and “Full-Strength CARMELO.” Current is what we’re using for the team’s next game and includes all injuries or rest days in effect at the moment. Full-strength is the team’s rating when all of its key players are in the lineup, even including those who have been ruled out for the season. This will help us keep tabs on which teams are putting out their best group right now, and which ones have room to improve at a later date (i.e., the playoffs) or otherwise are more talented than their current lineup gives them credit for.Game predictionsAs a consequence of the way we can generate separate depth charts for every team on a per-game basis, we can calculate separate CARMELO ratings for the teams in a matchup depending on who is available to play.For a given lineup, we combine individual players’ talent ratings into a team rating on both sides of the ball by taking the team’s average offensive and defensive rating (weighted by each player’s expected minutes) multiplied by 5 to account for five players being on the court at all times. Those numbers are then combined into a generic expected “winning percentage” via the Pythagorean expectation:Winning Percentage=(108+Team Offensive Rating)14(108+Team Offensive Rating)14+(108−Team Defensive Rating)14Winning Percentage=(108+Team Offensive Rating)14(108+Team Offensive Rating)14+(108−Team Defensive Rating)14That number is then converted into its Elo rating equivalent via:CARMELO Rating=1504.6−450×log10((1/Winning Percentage)−1)CARMELO Rating=1504.6−450×log10((1/Winning Percentage)−1)From there, we predict a single game’s outcome the same way we did when CARM-Elo was in effect. That means we not only account for each team’s inherent talent level, but we also make adjustments for home-court advantage (the home team gets a boost of about 92 CARMELO rating points), fatigue (teams that played the previous day are given a penalty of 46 CARMELO points), travel (teams are penalized based on the distance they travel from their previous game) and altitude (teams that play at higher altitudes are given an extra bonus when they play at home, on top of the standard home-court advantage). A team’s odds of winning a given game, then, are calculated via:Win Probability=1/(10−(CARMELO Differential+Bonus Differential)/400+1)Win Probability=1/(10−(CARMELO Differential+Bonus Differential)/400+1)Where CARMELO Differential is the team’s talent rating minus the opponent’s, and the bonus differential is just the difference in the various extra adjustments detailed above.Season simulations and playoff adjustmentsArmed with a list of injuries and other transactions for the entire league, our program can spit out separate CARMELO ratings for every single game on a team’s schedule. For instance, if we know a player won’t be available until midseason, the depth-chart sorting algorithm won’t allow him to be included on a team’s roster — and therefore in the team’s CARMELO ratings — until his estimated return date.Those game-by-game CARMELO ratings are then used to simulate out the rest of the season 50,000 times, Monte Carlo-style. The results of those simulations — including how often a team makes the playoffs and wins the NBA title — are listed in our NBA Predictions interactive when it is set to “CARMELO” mode.It’s important to note that these simulations still run “hot,” like our other Elo-based simulations do. This means that after a simulated game, a team’s CARMELO rating is adjusted upward or downward based on the simulated result, which is then used to inform the next simulated game, and so forth until the end of the simulated season. This helps us account for the inherent uncertainty around a team’s CARMELO rating, though the future “hot” ratings are also adjusted up or down based on our knowledge of players returning from injury or being added to the list of unavailable players.For playoff games, we make a few special changes to the team CARMELO process explained above. For one thing, teams play their best players more often in the playoffs, so our depth-chart algorithm has leeway to bump up a player’s MPG in the postseason if he usually logs a lot of minutes and/or has a good talent rating. This year, CARMELO outputs a separate recommended-minutes-per-game projection for both the regular season and the playoffs.We also have added a feature whereby players with a demonstrated history of playing better (or worse) in the playoffs will get a boost (or penalty) to their offensive and defensive talent ratings in the postseason. For most players, these adjustments are minimal at most, but certain important players — such as LeBron James — will be projected to perform better on a per-possession rate in the playoffs than the regular season. (Truly, he will be in “playoff mode.”)And we continue to give a team an extra bonus for having a roster with a lot of playoff experience. We calculate a team’s playoff experience by averaging the number of prior career playoff minutes played for each player on its roster, weighted by the number of minutes the player played for the team in the regular season. For every playoff game, this boost is added to the list of bonuses teams get for home court, travel and so forth, and it is used in our simulations when playing out the postseason.The complete history of the NBAIf you preferred our old Elo system without any of the fancy bells and whistles detailed above, you can still access it using the NBA Predictions interactive by toggling its setting to the “pure Elo” forecast.This method still has the normal game-level adjustment for home-court advantage, but it doesn’t account for travel, rest or altitude; it doesn’t use a playoff-experience bonus; and it has no knowledge of a team’s roster — it only knows game results. It also doesn’t account for any offseason transactions; instead, it reverts every team ¼ of the way toward a mean Elo rating of 1505 at the start of every season. We use a K-factor of 20 for our NBA Elo ratings, which is fairly quick to pick up on small changes in team performance.You can also still track a team’s Elo rating in our Complete History of the NBA interactive, which shows the ebbs and flows of its performance over time. This number won’t be adjusted for roster changes, but it should remain a nice way to visualize a team’s trajectory throughout its history. Version History4.0 CARMELO updated with the DRAYMOND metric, a playoff adjustment to player ratings and the ability to account for load management. Pure Elo ratings now use a K-factor of 20 in both the regular season and the playoffs.3.1 Estimated overnight ratings added as a stopgap between game results and data updates.3.0 CARMELO is introduced to replace CARM-Elo. Pure Elo ratings are adjusted to have variable K-factors depending on the stage of the season being predicted.2.1 CARM-Elo is modified to include a playoff experience adjustment.2.0 CARM-Elo ratings are introduced. Seasonal mean-reversion for pure Elo is set to 1505, not 1500.1.0 Pure Elo ratings are introduced for teams going back to 1946-47. Related ArticlesThe Complete History Of The NBAHow We Calculate NBA Elo RatingsThe Best NBA Teams Of All Time, According To EloHow Our 2015-16 NBA Predictions WorkWhy The Warriors And Cavs Are Still Big FavoritesFrom The Warriors To The Knicks, How We’re Predicting The 2018-19 NBA
Ohio State freshman forward Kaleb Wesson (34) goes up for the jump ball to start the game against Maryland on Jan. 11 in the Schottenstein Center. Credit: Jack Westerheide | Photo Editor
Scottish national team manager Peter Grant praised the 27-year-old ahead of the national team’s next two matchesFor Scotland national team boss Peter Grant the chance to have Fulham midfielder Tom Cairney on his team is a great opportunity.The national team will play a friendly against Belgium and then will start the UEFA Nations League against Albania.“The biggest skill he’s got is the fact he is comfortable in possession of the ball. Fulham play in a certain style, they have done it for the last few years, and Tom fits into it fantastically well,” he said to STV.“He is very much a creator of opportunities, he is also a goalscorer and he has proved that over the past couple of years – player of the year and Championship team of the year.”“I think the Costa Rica game, we didn’t see the best of him. You need people running by Tom so he can step in and get on the ball,” he added.Scotland needs a hero: Billy Dodds Manuel R. Medina – September 10, 2019 According to former striker, Billy Dodds his country needs a hero to inspire future generations as the team’s hope to qualify to the EURO 2020 is small.“We have got to build a team round about the facts that help these guys be at their best.”“To create space, other people have got to move out your space, and give the ball early. He picks up fantastic spaces between the lines and turns and faces the opposition,” he added.“In that particular game we didn’t do that quickly enough, we passed square first half. To get the best out of Tom you have to have the right players round about him, and that’s what we think we have.”“He is more than capable and the great thing he is playing at the highest level, playing against top international players week in, week out,” he commented.“That can only make you think better. He is training with a group of players now at Fulham which is much stronger than last season, we spent £104million.”
Felix Brych was in the center of attention in Valencia, as he sent off Cristiano Ronaldo and showed three penalties. Miralem Pjanic converted two of them in Juventus’ 2-0 win. Valencia starting lineup: Neto, Ruben Vezo, Gabriel Paulista, Jeison Murillo, Jose Luis Gaya, Daniel Wass, Dani Parejo, Carlos Soler, Goncalo Guedes, Rodrigo, Mitchy Batshuayi Juventus starting lineup: Wojciech Szczesny, Alex Sandro, Giorgio Chiellini, Leonardo Bonucci, Joao Cancelo, Sami Khedira, Blaise Matuidi, Miralem Pjanic, Cristiano Ronaldo, Federico Bernardeschi, Mario MandzukicIn the 11th minute, Mario Mandzukic had a nice chance to score after being left alone at the far post during a cross, but sent the ball over the bar. In the 17th minute, Sami Khedira had an even easier chance to score. Ronaldo sent a great cross with his left foot. Bernardeschi got at the end of it at the far post and sent the ball back to a wide open Khedira in the middle. He could pick and choose where to shoot and somehow managed to miss the entire net. It was a huge opportunity for Juventus, their second in the opening 20 minutes.Gigantic chance number three came in the 21st minute. Neto made a mess of Alex Sandro’s cross. He made a great save on Bernardeschi’s attempt to fix his mistake initially, but then dropped the ball straight to Matuidi. The Frenchman fired a shot which was blocked by an alert Jeison Murillo. Valencia was very lucky to avoid being down. Seconds later, Khedira dropped to the ground complaining of a leg injury. Massimiliano Allegri was forced into making his first change – Emre Can for Sami Khedira.Rumour: Juve put a price on Pjanic George Patchias – August 28, 2019 Juventus have put an 80 million Euro price tag on Miralem Pjanic.According to Tuttosport, and Caliciomercato, the Italian giants are ready to sell there…Tears of anguish were dripping from Cristiano Ronaldo’s face in the 30th minute when he received a shocking red card for patting Murillo on the head. The Colombian was the one who provoked Ronaldo and the Portuguese reacted somewhat angrily, but he didn’t deserve a straight red. It was an incredibly frustrating decision by an experienced referee such as Felix Brych. Where is VAR when you need one?In the 39th minute, Batshuayi stole the ball from Chiellini inside Juve’s box, made a great turn and fired a shot directly at a ready Wojciech Szczesny. Just as Valencia were starting to gain confidence, Juventus took the lead. Another Alex Sandro cross came to a unmarked Joao Cancelo, who missed a volley at first, but then reset himself on the left foot and rocked the cross bar. The rebound was headed away by one of Valencia’s defenders and it came to Parejo, who raised his boot way too high and kicked Cancelo in the face, leaving no choice to Brych but to show on the penalty spot. A really clumsy challenge by The Bats’ captain. Miralem Pjanic took responsibility in the absence of Ronaldo. Neto got a hand on the ball, but it wasn’t enough. Pjanic’s effort was too precise. 1-0 Juventus.In the 50th minute, Brych awarded another penalty to Juventus. This time around, it was a rather generous one. Murillo got tangled with Bonucci during a corner kick, yet the referee saw enough to show on the spot for a second time. Pjanic stepped up and hit the exact same spot again, only this time with even more power. 2-0 Juventus. Ruben Vezo, who was cautioned moments earlier for a foul on Matuidi, was replaced by Denis Cheryshev in the 57th minute. In the 65th minute, Szczesny had to stretch to save Carlos Soler’s long-range effort headed towards the bottom right corner.Following the subsequent corner kick, Douglas Costa replaced Miralem Pjanic. In the 70th minute, Marcelino made two more substitutions, taking off Batshuayi and Guedes and sending Kevin Gameiro and Santi Mina onto the pitch. Juve were doing their best to strangle the life out of the game and they were succeeding. Rodrigo should’ve scored in the 80th minute. Dani Parejo found him from a corner kick, but the Spanish international hit the ball with his shoulder instead of his head. Seconds later, Soler had another attempt on goal go begging.Allegri’s team was defending with ten men behind the ball and blocking every shot Valencia’s players took. Douglas Costa dribbled past Gabriel and stung the hands of Neto in the 85th minute. The Brazilian stayed on the ground following Gabriel’s robust challenge and couldn’t continue. Center back Daniele Rugani was the one who came in his stead. Rodrigo missed another free header in the 93rd minute. Two minutes later, Brych gifted a penalty to Valencia, but Parejo failed to capitalize. The miss summed up a miserable night for the captain and his teammates. After six minutes of stoppage time, the game was over. Marcelino has a lot to think about, as his team was outplayed and beaten by an undermanned Juventus.
Liverpool’s goalkeeper Alisson, who signed in the summer for £67 million, made a jaw-dropping stop to keep Napoli’s Arkadiusz Milik from scoring in injury time.Jurgen Klopp was impressed with his squad’s overall performance in Tuesday’s 1-0 victory over Napoli but was especially proud of Alisson. The Brazilian goalkeeper secured the win after Mo Salah put Liverpool ahead in the first half.“The goalie made the save of the season. I have no clue how he made that save. Thank God we have him,” Klopp told Viasport, as quoted by Goal.“If I’d known how good he was, I’d have paid double.“I think nobody expected a save in that situation. It’s a goal, no,” Klopp joked.Crouch: Liverpool could beat Man United to Jadon Sancho Andrew Smyth – September 14, 2019 Peter Crouch wouldn’t be surprised to see Jadon Sancho end up at Liverpool one day instead of his long-term pursuers Manchester United.“He had a lot of things to do tonight but how cool and calm he was, says a lot. But still he needs the other boys around, so he doesn’t have to make 100 saves like that.Klopp went on to laud the rest of his squad, saying they could have probably scored more goals because of the amazing fan support at Anfield Stadium.“It was unbelievable, what the boys they did tonight, it was so special. They created such an incredible atmosphere I am still full of adrenaline about it, just unbelievable.”“Yes we could have scored more,” he added. “The most difficult period was just after the 1-0. That made the game intense.“We knew we could not change the tactics. After 65 minutes, it was really wild, counter and counter without finishing. I am really proud of what the boys did.”