News News FranceEurope – Central Asia The central Paris court handed down a first explicit decision on the Law on confidence in the digital economy (LEN), that judges an Internet hosting provider’s responsibility for content. The case demonstrated the difficulty of applying the concept “manifestly illegal” content, as introduced by the LEN. Follow the news on France Help by sharing this information RSF denounces Total’s retaliation against Le Monde for Myanmar story November 17, 2004 – Updated on January 20, 2016 Law on confidence in the digital economy (LEN): first jurisprudence on the responsibility of hosting companies picks holes in concept of “manifestly illegal” content FranceEurope – Central Asia News to go further “We’ll hold Ilham Aliyev personally responsible if anything happens to this blogger in France” RSF says June 4, 2021 Find out more Receive email alerts Use the Digital Services Act to make democracy prevail over platform interests, RSF tells EU May 10, 2021 Find out more News June 2, 2021 Find out more The central Paris court on 15 November 2004 handed down a first explicit decision on the Law on confidence in the digital economy (LEN), that judges an Internet hosting provider’s responsibility for content – and found in favour of the provider.The case concerning the 1915 Armenian genocide and pitting the Armenian National Committee (CDCA) against the Turkish consul in France and Wanadoo, demonstrated the difficulty of applying the concept “manifestly illegal” content, as introduced by the LEN law.The CDCA had laid a complaint against Turkish consul in Paris, Aydin Sezgin, and against Wanadoo in connection with articles contesting the Armenian genocide on the consulate’s website (http://perso.wanadoo.fr/tcparbsk/). The court explicitly referred to LEN in assessing Wanadoo’s responsibility “in the light of the interpretation contained in the decision of the Constitutional Council on 10 June 2004”.Reporters Without Borders has constantly campaigned against the responsibility of providers established by LEN. Since the law’s adoption, it has urged judges to show extreme vigilance in its interpretation. “The law imposes on technical providers the obligation to decide on the legality of content to which they provide access and effectively to take over the work of the courts. The Constitutional Court that ruled on the law had limited the responsibility of providers by introducing the concept of ‘manifestly illegal’ content. But the CDCA case against Wanadoo demonstrates that the concept is too vague to effectively protect freedom of expression. Deciding on the legality of content turns out to be an arduous business, which cannot be accepted by providers, particularly smaller ones,” said the organisation. The entire case rested on the question: “Does contesting the Armenian genocide constitute a manifestly illegal act? To reach a decision the court had to examine a variety of national and international legal texts produced by the Armenian association. These were: the 1881 press law; the statutes of the international military court annexed to the London agreement of 8 August 1945; the French law of 29 January 2001 recognising the Armenian genocide; the international convention for the prevention and repression of genocide adopted by the UN on 9 December 1948 and an 18 June 1987 resolution of the European Parliament. The judges even went so far as to check the minutes of parliamentary sittings.The court finally decided that nothing in these texts confirmed that contesting the Armenian genocide was manifestly illegal. The decision exonerated the provider from any responsibility in the case. Nevertheless the court had to examine three international texts and two French laws to reach a decision. What would have been the judges’ decision if one of the international conventions stipulated that Armenian genocide was not contestable? Would the court have then decided that providers were responsible. The question is far from rhetorical. The judgement given seems to suggest that the provider should, if faced with this type of content, check its legality in the light of national laws and also international legal texts. Certainly ignorance of the law is no excuse but is it reasonable to believe that providers, particularly the smaller ones would be able to handle such a task?According to Lionel Thoumyre, who works for the French consultative group ‘Internet Rights Forum’, the interpretation of the concept “manifestly illegal” should only be applied in relation to the degree of legal competence that one could expect from a provider. With this kind of interpretation, it would be easier to prove that the provider, who could not be considered an expert in international law, was not responsible in this case. It seems however that this ruling, even if favourable to the provider, demonstrates more fundamentally that the legal responsibility on providers is too heavy, despite the protection of the phrase “manifestly illegal”. In wanting to put companies in the place of judicial authorities, the legislator has opened a Pandora’s Box the consequences of which will gradually make themselves felt. The risk of advance censorship of the Internet by its technical providers remains on the agenda.Responsibilities established by the LENThe Constitutional Council made its ruling on 13 June 2004, on the Law on confidence in the digital economy. It reaffirmed the principle of legal responsibility of providers in cases where the illegal content has been drawn to their attention. Even though this step established a system of private justice on the Internet, the council took the view that it conformed to the European directive on which the French law was based. It however toned down the law as voted by parliament, to the effect that providers could only be held responsible if a judge had ruled the content illegal or if a web page was “manifestly illegal”. This last point picked up a recommendation from the Internet Rights Forum, providing judges with limits to this law of responsibility. This meant that French jurisprudence only recognises as “manifestly illegal” content involving revisionist statements, child pornography, justification of war crimes and so on. It therefore became unlikely that service providers would be convicted for posting defamatory articles, for example. RSF_en Organisation
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A DOG found by a reader in Lismonaghan, Letterkenny, last night has been reunited with her owner. Turns out she’s called Susie – and her owner is “delighted” she’s back home.Thanks to all readers for your responses. LOST POOCH SUSIE RE-UNITED WITH OWNER was last modified: December 28th, 2013 by John2Share this:Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on LinkedIn (Opens in new window)Click to share on Reddit (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Telegram (Opens in new window)Click to share on WhatsApp (Opens in new window)Click to share on Skype (Opens in new window)Click to print (Opens in new window)
Mark Hughes is remaining tight-lipped over reports he is looking to pip West Bromwich Albion to the signing of Birmingham goalkeeper Ben Foster.The England international is on loan at The Hawthorns and the Baggies are keen to sign him on a permanent basis at the end of the season.But Hughes has expressed some interest in taking Foster to west London in the summer if QPR stay in the top flight.Meanwhile, Rangers are one of a number of clubs who have watched Birmingham’s highly-rated young keeper Jack Butland, who was recently linked with a move to Manchester United.“There’s speculation about players but we’re not thinking in those terms yet,” said Hughes.“A lot of players’ names get banded about. I can’t confirm or deny anything really. At the moment my focus is on getting the points we need to stay in the Premier League.”Click here for our QPR quizFollow West London Sport on TwitterFind us on Facebook
Learn how to create V-Ray materials in this Cinema 4D video tutorial.While the built-in renderers in Cinema 4D may be powerful, V-Ray offers users the ability to render more realistic textures at faster speeds. If you’ve spent time working with the V-Ray renderer in Cinema 4D than you know that there are hundreds of individual controls designed to give you the most customization possible.All of these controls can be confusing, especially if you are new to the rendering process. Thankfully we’ve found an awesome tutorial covering the basics of creating V-Ray materials in Cinema 4D. In the following video tutorial created by Kamel Khezri he demonstrates advanced features in the V-Ray material editor. The tutorial covers:Creating texturesWorking with lightsSelecting transparencySetting reflection mapsV-Ray is a third party plug-in, but widely considered an industry standard. If you are interested in learning more about V-Ray we recommend checking out the official website.This video was first shared by Kamel Kherzri on his Vimeo channel. Thanks for sharing Kamel!Want to see more Cinema 4D video tutorials? Check out the Cinema 4D section of the PremiumBeat blog.Have any other tips for working with advanced V-Ray materials? Share in the comments below.Bonus: Check out these FREE V-Ray Material ResourcesV-Ray MaterialsFlying Architecture MaterialsV-Ray Materials UK
Actor Ranbir Kapoor, who has stakes in the Mumbai City Football Club (FC) franchise of the Indian Super League (ISL) football tournament, believes Bollywood celebrities’ involvement in sports in the country is beneficial in many ways.Apart from Ranbir, the ISL has star team co-owners such as John Abraham and Abhishek Bachchan, while Varun Dhawan endorses the Goa team.”It’s a great thing, and every sport should be encouraged in the country. Cricket is our first love, but that doesn’t mean we should not give kabaddi or football a platform,” the 31-year-old said here recently at the launch of his football club.”More than anything, you are giving opportunities to the youth to become a football player or kabaddi player and not necessarily a cricketer,” he added.Apart from Ranbir, the ISL has star team co-owners such as John Abraham and Abhishek Bachchan and Varun Dhawan.Abhishek, whose team Pro Kabaddi League franchise Jaipur Pink Panthers, won Sunday night, is also part-owner of the Chennai franchise of the ISL.John Avraham is the co-owner of NorthEast United FC, while Varun Dhawan is the brand ambassador of FC Goa.Ranbir stated that everyone involved in the ISL will strive to make the league engaging and hopefully it will lead to the creation of a very good Indian football team.”It’s about the quality of the game and how well Star Sports markets the game. All the people who are involved – like IMG Reliance, Hero, franchise owners and everyone, wants to put their best foot forward and create a league that is engaging and entertaining and has quality football.advertisement”Hopefully we can have our own Indian football team which can play in the World Cup,” Ranbir said.The inaugural season of the Hero ISL will be played from Oct 12 to Dec 20 in eight Indian cities. Bollywood star Salman Khan is a co-owner of the Pune franchise.
About the authorPaul VegasShare the loveHave your say Man City midfielder Sterling: Liverpool ideal for Salahby Paul Vegas10 months agoSend to a friendShare the loveManchester City midfielder Raheem Sterling isn’t surprised by the success of Mohamed Salah at Liverpool.Sterling says former club Liverpool is the ideal stage for the Egyptian.Speaking with 360 Sport, Sterling said: “I’m not surprised (by how well Salah’s done).“He’s gone to Liverpool and it’s clicked for him.”Every player has a moment when they are good, very good and then it clicks. He’s been exceptional.”But it’s about how you maintain it – and he’s maintained that perfectly and that’s a credit to him.”
Abidjan – The ongoing commitment of Morocco’s King Mohammed VI to promoting South-South cooperation as a development tool serving the people of sub-Saharan Africa was shown once again on Tuesday through real estate projects of over 8,000 low-cost housing units, launched in Abidjan by the Sovereign, who was accompanied by Prime Minister of the Republic of Côte d’Ivoire, Daniel Kablan Duncan.These projects are in line with the royal speech on the occasion of the opening session of the Ivorian-Moroccan Economic Forum, in which the Moroccan monarch said that Africa does not need assistance as much as more mutually beneficial partnerships. “Our continent needs human and social development programs more than humanitarian aid,” the King said.The projects, which reflect the determination of the King to allow the countries of the African continent to benefit from the highly appreciated Moroccan experience in the fight against unhealthy housing, involve the construction of 7,500 housing units in the Locodjoro neighborhood (Commune of Attécoubé ) and 530 other units in the city of Kumasi. Part of the efforts of the Ivorian government to address the structural deficit in housing, these two projects, carried out by the Moroccan real estate group Addoha, will cost a sum of 2.2 billion dirhams and will cover a total area of 29 hectares.The Locodjoro project (26 ha) includes the construction of an integrated city that combines tradition and modernity with 7500 low-cost housing units and community services (a shopping mall, schools, a cultural center, a business center, a police station), and green spaces.Located in the heart of the city of Kumasi, the Kumasi project (3 ha) provides for the construction of 530 affordable apartments and a school.These projects will enable the Abidjan population, particularly those with limited incomes to access decent housing at favorable conditions.These two real estate projects symbolize the historical relations of friendship and brotherhood between the two countries, as well as the unwavering commitment of Morocco to supporting the economic and social development in the Republic of Côte d’Ivoire, as part of win-win cooperation.On his arrival, King Mohammed VI was greeted notably by the Ivorian Minister of Housing and Urban Development, the Mayor and MP of the City of Kumasi, the CEO of Addoha Group, and the employees and managers of the projects launched.1 dollar= 8.18 dirhams
Receiver Plaxico Burress said in a radio interview earlier this week that, “no doubt about it,” he wants to play next season for the Carolina Panthers, who are quarterbacked by last year’s rookie of the year, Cam Newton.But Burress has received calls from other NFC South teams, the Panthers haven’t called yet, he told WFNZ in Charlotte on Monday.“My brother lives right there in Charlotte, my cousin lives there in Charlotte, and all my family is there in Columbia, South Carolina. It would just be a great situation all the way around and being close to everybody.“Not just that, but playing with Cam, who I think is one of the top five quarterbacks in all of football, and as long as I can remember, Steve Smith, has been one of my favorites. I just think it’s a great situation.”Burress told WFNZ that NFL teams are interested in him but he hasn’t found the right fit. In February, Burress had expressed a strong desire to sign with the Philadelphia Eagles, but there apparently doesn’t seem to be a match between the team and receiver, as he remains unsigned.“I just want to put myself in a position to go out and have success and to make the guys around me better and obviously play with a quarterback who can really play the position,” Burress said. “It’s just about me picking my spots and seeing where I’m a good fit. You just look at a situation like Carolina. You can’t come across a better situation than that.”Burress returned to the NFL last season after missing two seasons thanks to a prison sentence for accidentally shooting himself in the leg at a nightclub.Burress, 34, tied for the Jets’ lead with eight touchdown receptions but never really clicked with quarterback Mark Sanchez, finishing with 612 yards and 45 receptions.
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