Let me start with a brief introduction on the topic of Shot Influence, or Attacking Influence, or whatever it may end up being named. What is Shots Influence? An attempt at quantifying the importance of a single player to his teams attacking output. How is it calculated? We combine player X's shots + shot assists and divide that number against the teams shots output when the player was on the field of play. EG: Player X had 4 shots and 1 shot assist. His team recorded 10 shots in that game. Player X was responsible, in some way, for 50% of his teams attacking output. Origins of Shots Influence Well, two people were sat watching Man City v Bayern Munich and became fascinated with which Bayern players were creating the most shots for their team and how that influence may look as a percentage. The genesis for that Man City v Bayern conversation was, for me, sparked by Benjamin Wendorf's work on 1980's hockey players and their shots contributions. Why is Shots Influence of interest to me? It may not be! But, I am going to try and make it interesting. Shots Influence,I believe, can tell us some pretty cool things about how important a player was to his teams attacking output. If a player has a high shots influencepercentage then we may conclude that he is very important to his team when on the field of play and something I called a 'hub player' - a player that is always involved in the attacking scheme in terms of taking shots or creating shots. Benchmarks It's way too early to be drawing any conclusions as to what numbers may separate an average player from a good player, and a very good player from a Messi. If pushed I would say that if a player is responsible for ~40% of his teams attacking output in terms of shots, then it's likely that he is a mighty good player. Previous Shots Influence work Seven weeks of Man City data <LINK> Ronaldo <LINK> Messi <LINK>   Right, that's the boring stuff out of the way! I want to use three examples of players performance from the 2012/13 season to highlight how influential certain players are and how that influence can change over the course of the season. Suarez, Bale and Messi are the three examples I want to use. All three players were excellent in 2012/13, all posted really high shots influence numbers. Let's begin.

All Graphs Are Interactive

Suarez

Luiz Suarez had a monster season in 2012/13, which I had ranked as the 5th best season by a forward in the last 5 PL seasons. Still, during the summer, many Liverpool fans wanted to sell, to a certain extent, understandably, to a club that was willing to buy the player. 'Over-rated', 'inefficient', 'not worth the trouble' and 'Liverpool perform better without him' are just a small selection of words which used to criticize Suarez, and i don't entirely disagree with a lot of those words. That said, I was, and still remain, a big fan of Suarez the player. He's a 'hub player', constantly involved, almost always important to his teams attacking play when on the field. Hell, I even tried to convince @mixedknuts that he would be a good signing for Arsenal! Just how good is Suarez? Well, we need to see him to repeat last seasons level of performance before we anoint him as a genuinely consistent player who has world class ability. But my, was Suarez important to Liverpool's attack when he was on the field of play.

The chart above lists Suarez's game-by-game percentage contribution (red) and his rolling contribution. Suarez tailed off a little as the season wore on (Coutinho influence?) but Suarez's rolling attacking contribution as a percentage of Liverpool's attacking output is crazy good. When Suarez was on the field of play, 43.47% of Liverpool attacking output in terms of shots was created by Suarez. Suarez was the hub for Liverpool last year, he was vitally important when he did feature for Liverpool. Now, some of Suarez's high percentage contribution is due to his shot-happy nature and his willingness to shoot from anywhere. Suarez took 187 of Liverpool's 641 shots (29.17%) when on the field of play, but he also was a pretty good creator of shot opportunities for his team-mates: of non-Suarez shots the player was responsible for creating 90 shots assists of 551 shots (16.3%). Suarez was pretty important for Liverpool last season when he actually played. A 43.4% attacking contribution number is testament to that.

Bale

Injured, Welsh Ronaldo, shot monster. Those are the first three words that spring to mind when I think of the most expensive player in history. Bale is a fine, fine player who is an attacking monster with a skill set for the ages. Real Madrid overpaid, we all know that, but they still purchased a mighty good player. Bale's 2012/13 season seemingly convinced Madrid to pursue Bale, and what a season 2012/13 was.

Bale's rolling contribution improved as the season went on, and that may be testament to playing in the more influential central position that Bale occupied. Improvement in Bale's game was also a thing. Gareth Bale's final attacking contribution number stands at 43.23%, just a shade behind Suarez's. Only God knows what Bale's contribution number would have looked like if he had played centrally all season and not played injured in the last 5 or so games. Bale's shots only as a contribution percentage: 165 shots for 29.7%. Bale's shot assists for non-Bale shots: 75 shots assists for 19.2%. Yup, that 19% number doesn't really point to a player who couldn't create for his team-mates.

Messi

Initially, I wasn't going to include Messi's numbers in this feature, but I changed my mind. I think it's important to include the data from the footballjesus as a sort of impossible benchmark.

We know Messi is brilliant, we know he receives deferential passes from team-mates who are also excellent at creating space for him. But we didn't previously know that Messi was responsible for >50% of Barcelona's shots output when he was on the field of play.

FIFTY ONE PERCENT! We know Messi is a super freak, but I didn't expect Messi to post a contribution number that was so high, simply because the quality of his team-mates is of such a level that the shots output would be distributed a little more evenly.

Messi posted a 2012/13 number that is pretty hard to believe. Even with all the world class talent at Barcelona, He still stands out as a God amongst men.

Messi's shots only as a contribution percentage: 163 shots for 40.1%.

Messi's shot assists for non-Bale shots: 45 shots assists for 18.5%. With Messi on the field of play 40% of Barcelona's shots are taken by one player.

The Future

For the 2013/14 season I am tracking the shots influence numbers for 5 or 6 players from each PL team. Over the course of the season this should give us some nice information on who has been the most creatively important players for their respective teams. Suarez should be an interesting case in 2013/13. Which player stands out for Tottenham in Bale's absence will also be of interest.

I’m slightly short on time for the rest of this week because I’m off to Dublin for the weekend, but enough of you asked for the updated English Championship rankings from the predictive model, that I was moved to post them. If you have never read one of our predictive model pieces, check back here for a fairly thorough intro. One thing to keep in mind is that the model neither knows nor cares how many points each team has in the league table (though we attach them once points start to matter in who might actually finish where at around the halfway mark in the season), so some things might look very strange right now. That is to be expected. It also doesn’t know how much teams spent on players and payroll, so if a team is punching above or below their weight here, it’s on statistical merit alone. League Winner: Watford, Burnley Promotion Playoffs: Ipswich, QPR, Middlesboro, Forest, Leicester, Wigan, Derby… sort of Relegation Candidates: Yeovil. Barnsley, Doncaster, Millwall, Bolton At the top, the picture is pretty clear. Watford are the best team in the model , followed by Burnley. Burnley’s striker pairing of Ings and Vokes – who work as a comedy duo during the offseason – are notching damned impressive numbers. Check them out if you see a Burnley game on TV, they are worth a watch. If I’m honest, it feels like QPR are sandbagging. Only 2 goals conceded in 10 matches is ridiculous, but that team should be scoring a lot more than they are. It feels like Harry has told them they absolutely must be solid defensively, and that’s costing them at the offensive end. This is weird, because in the past Redknapp has been a fairly indifferent defensive manager (as indicated by his yet-unpublished manager fingerprint), while pretty much letting his talent figure it out on the offensive end.  Given the money disparity, QPR should be walking away with this league. Statistically they are fine, but Watford and Burnley are definitely better. A quick note on Derby: their coefficient is in flux since they fired Nigel Clough. Under Clough they were doing pretty much everything right statistically except winning the games. Under McClaren - well, we’ll see. It takes 5-6 matches before teams stabilize enough to have any clue as to how they will look. At the bottom end of the table, it looks like Yeovil are probably doomed. Their situation isn’t completely hopeless, but uh… At least they have Barnsley to keep them company? Bolton’s surface shot dominance looks okay, but they have real issues when you dig deeper, especially on the offensive end. Jermain Beckford (29!) is never the answer to fixing a team’s offensive woes. Neither is David Ngog. Bolton have both and could desperately use a loan or two up front from the Premier League. Movers and Shakers As you can see in the chart above, I’ve noted how much teams have moved up or down in the rankings. In the coming weeks, I’ll likely add this to Tableau for a prettier graphical representation, but for now you get the lazy version. As mentioned before, there is fairly reasonable volatility in the early weeks, but from here on out things will settle down quite a bit. Due to clustered coefficients, it’s not that hard for teams to wiggle around in the center of the rankings, but it gets tough to break into either the very top or bottom of the table without exceptional performances. Birmingham, Charlton, Forest and Boro were the big positive movers this time, with both Birmingham and Charlton leaping out of relegation range, and Boro and Forest showing themselves as playoff candidates. On a marked downward trend are Brighton, Huddersfield, Yeovil, and Blackburn. Yeovil were already worryingly bad before, but have plunged all the way to the bottom. The silver lining is that they can’t drop any further (though their coefficient can)!  Brighton went from playoff candidates to middle-of-the-pack stragglers, and Blackburn once again look like a team where Jordan Rhodes (9 goals already) will keep them afloat, even if the rest of the team is pretty meh. Huddersfield looked very much like the little team that could initially, but they have come back to the pack and are dead average overall (but very good defensively).  

If you read my Introduction to Manager Fingerprints, you know I’m pretty excited about this line of research. Statistical output for football teams is a function of both talent and tactical systems, but it appears that at least some managers (like Mark Hughes) end up with similar statistical production almost in spite of differing talent levels. This area of research is incredibly deep, because you can apply it to every single manager across every single league you have statistics for, both currently and historically.

It’s also tremendously interesting because it creates an actual consulting product that teams who are searching for new managers can and should use. You want to know the managers out there across leagues whose teams have the best (and worst) statistical output. In fact, I would immediately argue that teams trying to hire a new manager need to know these things just to prevent themselves from making big mistakes.

I’ve got a few more ideas I pitched to the guys at Opta that I want to write about, but here on StatsBomb I wanted to talk about one of the most exciting young managers in Europe – Andre Villas-Boas.

AVB

From Porto to Chelsea to Spurs - and nearly to PSG this summer - Andre Villas-Boas is a manager in demand. A fresh-faced 35 years old, when hired AVB comes complete with ginger beard, gravelly voice, and an alleged general disdain for stats. Yet he also employs a tactical system that produces statistical output that is an analyst’s wet dream, and it is wickedly consistent.

According to Wikipedia, for his first managing gig he took over Academica Coimbra when there were bottom of the league without any wins, in October 2009. By the end of the season, they were 11^th in the league, 10 points clear of relegation. Unfortunately I don’t have any stats from that season, but we do have the stats from his next stop.

Check this out.

This is AVB at Porto. Granted, those numbers are fuelled by the best talent in the Portuguese league, but they are tremendous. Shot Dominance of 1.92 (meaning they shoot they ball almost twice as often as an average opponent) is outstanding, as are the shots on target percentages, especially on the defensive side of the ball. 27% of opponent shots on target would almost always be one of the leaders in Europe for that metric. In his one season at Porto, AVB went undefeated in the league, won the Portuguese Cup, and the Europa League, not unlike his mentor Jose Mourinho.

In fact, I ran the rest of Porto’s stats from that year through the predictive model, and while the level of league competition is different, Porto graded out like Bayern Munich from last season. Cruising to the Europa League was no problem.

For reference, Porto had finished third the season before, 8 points behind Benfica. In AVB’s one season they won the league by 21 points. 2.82 points per game in a league season!

That summer, Roman Abramovich hired him away from Porto (paying 15M euros for the privilege) and made him the new Chelsea manager.

His time at Chelsea was (allegedly) fraught with personnel clashes. AVB’s system requires his team to employ a high line to allow selective defensive pressing within a confined space. This is especially true when employing two midfield destroyers, who hunt the ball and immediately recycle it to attacking players. He was also hired to help transition Chelsea away from their aging, Mourinho-built core into a newer, younger team. Unfortunately, John Terry and company were not very interested (or capable) of playing AVB’s preferred style, which resulted in all sorts of political stuff leaked from the dressing room and into the press.  It also resulted in way too many great chances for opposing attackers – note the shots on target conceded percentage - and AVB was eventually fired in March.

Chelsea would go on to win the Champions’ League title that season under assistant manager Roberto DiMatteo.

Things might not have worked out quite how he hoped, but you can see the outline of what AVB was working toward. Play the high line, employ faster center backs in defense, tighten up a bit, then drop the shots on target while pushing the shooting pace. Another year of personnel changes and he might have gotten there.

Note the incredibly large dive in Shot Dominance and overall effectiveness once DiMatteo took over the same personnel. They may have managed to grind out a Champions’ League title, but a team with those stats would never win the league.

Never one to overlook a quality bargain, Daniel Levy hired AVB as Harry Redknapp’s replacement that next summer, and this is what AVB’s teams have produced at Spurs.

Look familiar? This season should look even better, as Spurs parlayed Gareth Bale’s skills into talent upgrades all over the park.

Three different teams, three different groups of talent, two different countries, and yet the system is producing nearly the exact same thing year in and year out. Everything we had been told previously suggests that’s not supposed to happen. This type of statistical output is very often the product of teams that win the league. Granted, Villas-Boas has gone to teams with pretty good talent levels each time, but look at the difference between the stats Chelsea produced under him versus what they did immediately after under Di Matteo. That’s massive.

Look at what he did at Spurs with the same group of guys versus Redknapp, and you see nearly identical offensive shot stats, but the defense got much tighter. As a result, Shot Dominance ramped up. This year, you’ll probably see that improve slightly again.The key to winning the league, however, will be improving the shots on target numbers to closer what they were at Porto (Premier League average is about 32.5%). It could still happen, since Spurs have better personnel in nearly every spot except whatever role Gareth Bale was actually playing.

That said, they are still integrating the new players into the offense, and they still have some fairly serious issues defending set plays, which thus far in the season is hurting the shots on target numbers at both ends.

I would be shocked if Spurs don’t make the Champions’ League this year. Despite massive spending disadvantages compared to the rest of the top teams, they might even be a reasonable dark horse contenders to the league title.

This would not have happened under Harry Redknapp.

All data used in this piece is from Opta.

Abbreviation Guide

Shot PG – Shots For, per game

SOTPG – Shots on Target For, per game

SOT% - Shots on Target percent

ShAgPG – Shots Against, per game

SOTCPG – Shots on Target Conceded, per game (sometimes written as SOTCON)

SOTC% - Shots on Target Conceded percent

ShDom – Shot Dominance. This is a metric that looks at how many shots a team takes versus concedes against average competition. Greater than 1 means a team takes more shots than average opponents, less than 1 means they take fewer than the opponent. This is a different way of working with TSR (Total Shots Ratio)

Poss – Possession Avg_PPG – Average points per game in the league table for that season.

This article was originally published on the OptaPro Blog. Managers get a bum rap. For guys allegedly only responsible for 15-20% of a team’s performance (Soccernomics), they tend to catch a ton of flack when things go wrong, while the players catch most of the praise when things go right. Most managers are compensated fairly handsomely for their burden, but for a job that some view as simple as choosing which players to play each day, it seems pretty harsh. Personally, I’m unconvinced managers have so little influence. Players matter a lot, but so do tactical systems. Give Sam Allardyce an identical set of players as Pep Guardiola, and you will get substantially different statistical output. You might even get different game outcomes (win/lose/draw). In fact, that’s one of the things that bothers me about football - there is only one way of evaluating managers. Results. Did you win enough, Y/N? Obviously you can contextualize that a bit and investigate whether a manager won enough versus expectation, or versus monetary expenditure or whatever, but at the end of the day results are the only widely used method to determine whether a manager is doing a good job. This is a problem. Think of it another way… You are the owner of an English Premier League football team. Your manager has taken a new job and now you need to search for a new manager. How do you evaluate candidates for the position? Points per game at past jobs? That’s it? Considering that the person you hire has an enormous influence on a company/investment that is likely worth hundreds of millions of pounds, that’s a little terrifying.  What if they were lucky before? What if they inherited a ton of talent (and money) compared to other teams in the league and rode that to positive results? Additionally, how can an owner – someone who is probably not completely immersed in football – know about what style of play a particular manager uses? Or 20 managerial candidates? Remember when Owen Coyle was branded as playing expansive passing football at Bolton, and then Zonal Marking did a tactical review and discovered that Bolton were among the league leaders in long balls? That type of thing happens all the time. Figuring out signal from noise in that environment is nearly impossible. A Statistical Framework This is where stats come in to play. Whenever I want to evaluate something intelligently, I always come back to the data. Managers influence the way their teams play via tactics. Some teams sit deep and rely on set piece expertise to grind out wins (Stoke under Tony Pulis). Some teams pass their opponents into oblivion (Barcelona under Pep and Villanova). Other opponents play fairly normal, positive football until they score, at which point they shell and unleash lightning counter-attacks. All of these different tactical variations affect the stats teams produce. By focusing on statistics that actually matter (ones that have a positive correlation to points produced), we can create a “managerial fingerprint” that tells you how future teams a particular person manages are likely to perform. What Matters? For now I want to focus on five simple stats, four of which involve shooting and one that provides context and hits heavily on passing. The shooting stats are Shots For per game, Shots Against per game, and then the percentage of Shots on Target, for and against.  These stats take a step back from goal differential, but still provide a fairly accurate picture of what is happening in the areas of the pitch that matter. The contextual stat is possession. Some systems need heavy possession to create chances, while other ones prefer to set up a bunker defense and then attack quickly out of it. Possession tells you a lot about how a manager wants their team to play (and at least a little about what type of football you’ll be giving to fans). All of these stats correlate with winning, but there’s one other thing you need even out, and that’s the league dynamic. Teams in the Bundesliga and Spain average a much higher percentage of shots on target than those in England. Therefore a manager whose team is allowing 33% Shots on Target against in Bundesliga, is actually 2-3% better than league average. The same number in England would be a half percent worse than the league average, so league context remains important. An Example I’m going to delve into additional specific managers here on OptaPro in the future, but today I wanted to look at the fingerprint for one of the most well-travelled managers in recent Premier League history: Mark Hughes This is Mark Hughes at Blackburn. At the bottom there you can see the 38 games prior to when Hughes came in, including a disastrous 5 game stretch under Souness before he upped sticks and moved to Newcastle. The Shot Dominance (ShDom) numbers under Hughes are solid, and they would generally produce a happy midtable side for just about any team. They were mostly indifferent toward possession, basically floating around 51% the entire time. [Note: There’s a legend that explains all the abbreviations at the bottom of this article.] However, EPL Shot on Target percentage is usually around 32.5%, and the Shots on Target Conceded percentage (SOTC%) in the second two years of Hughes’s reign would be worryingly high and possibly indicate a need to invest in better personnel there. In fact, the year after Hughes left, Rovers finished 15^th, while conceding the third most goals in the league. Hughes was hired during the summer of 2008-09 to help take Manchester City on to bigger and better things. The personnel he had at City was considered much better than what he had at Blackburn, and obviously given the Abu Dhabi takeover, they spent much more money as well. His first season included players like Robinho, Joe Hart, Micah Richards, Vincent Kompany, Pablo Zabaleta, and Craig Bellamy. By the start of Hughes’ second season in charge, he had also had Gareth Barry, Carlos Tevez, Adebayor, Kolo Toure, and Joleon Lescott on the playing staff. That’s just weird. Despite talent that was massively better than what Hughes had at Blackburn, City’s statistical profile looks almost identical to the last place Hughes managed with one major difference – the shots on target percentages on both sides of the ball were much better. That’s it though. The tempo they played was a little faster (a touch more shots for and against), but given the different talent, I would expect the ShDom and possession numbers to differ much more than they do. The ShDom numbers in particular though, are quite a bit different than the football City produced under Sven Goran Eriksson. The next stop on the Mark Hughes Managerial Tour was London, where Hughes took over Fulham after Roy Hodgson bounced off to West Brom (via Liverpool *ducks*). From Blackburn to the nouveau riche of Manchester City and back to Fulham, and the underlying numbers remain the same. The Shots on Target for the offense is lower (in my opinion, this is again probably a result of talent disparity), but that is it. Finally, we have QPR. Hughes replaced Neil Warnock midseason and was there for 30 matches before being replaced himself by Harry Redknapp. Neil Warnock didn’t look like he could save this club. Mark Hughes never really improved them during his 30 games in charge bar the possession numbers. Nor did Harry Redknapp, whose shot dominance and shots on target numbers were basically the same as Hughes.  I can’t really explain this one except to say that QPR just couldn’t do anything well enough on either side of the ball to survive, and none of these managers was clever enough to figure out how to fix them/keep them in the Premier League. Mark Hughes’ Statistical Fingerprint Looking at all of this – a little over eight full seasons of data – I would conclude that Hughes is an average manager. He’s able to consistently produce a style of football that is likely to finish in the middle of the table. When presented with a hugely talented team at Manchester City, he was unable to find a way to further maximize their skills. The average shot dominance number for his career is bang on 1, and both shots on target for and against are around the league average as well. Statistically, Mark Hughes is almost the epitome of the average Premier League manager. Some people would probably roll their eyes at this. “Oh boy, you found someone average!” In reality, this is kind of a big deal. If you hire Mark Hughes, at least you know you aren’t making a mistake. Teams in the top half of the table that want to improve probably don’t want him, but if you are a bottom half team attempting to simply secure regular survival, Hughes seems fine. Sometimes fine is good enough. Managerial Fingerprint Conclusions The point of all this is that teams need additional methods to evaluate managers beyond “did they win enough?” That sliver of information is too thin to make judgments where tens or hundreds of millions of pounds can ride on the outcome (especially given the cost of firing managers and coaching staffs these days). Going beyond the points per game approach and looking at the underlying statistics that matter is the first step in building a new framework that will help football organizations evaluate current personnel as well as future managerial hires. It can also help currently employed managers know the areas they need to improve better. This information may seem fairly basic right now (and obviously plenty of additional statistics can be layered on top of it), but despite the simplicity, it captures stats that have a significant relationship to winning. There are plenty of additional questions that this framework can help us answer. Here are just a few:

• Do elite managers tend to dramatically change the statistical output of teams when they take over?
• When teams hire new managers, does statistical performance improve in the second season? (Given the average manager only last something like 1.5 seasons these days, this is kind of a big deal.)
• Can new managers elevate or decrease the performance of identical talent?
• Review past managerial hirings and firings to determine whether they made solid statistical sense, or whether they were destined to fail from the start.

Using the managerial fingerprint framework, I will start to delve into some of these topics on future posts here and on StatsBomb.com. It’s far from finished, but it certainly provides additional data for making better managerial decisions. --TK Shot PG – Shots For, per game SOTPG – Shots on Target For, per game SOT% - Shots on Target percent ShAgPG – Shots Against, per game SOTCPG – Shots on Target Conceded, per game (sometimes written as SOTCON) SOTC% - Shots on Target Conceded percent ShDom – Shot Dominance. This is a metric that looks at how many shots a team takes versus concedes against average competition. Greater than 1 means a team takes more shots than average opponents, less than 1 means they take fewer than the opponent. This is a different way of working with TSR (Total Shots Ratio) Poss – Possession Avg_PPG – Average points per game in the league table for that season.

Stoke assistant manager Mark Bowen was doing the rounds last week telling BBC Sport: "We've done passing drills on the training ground and small games to encourage the players to stay on the ball that little bit longer." Even before the season began Bowen was keen to make it known Stoke were going for a style change telling Sky Sports News: "We want the players to know they can play the ball, that mistakes will be made, but that we won't come down on them like a ton of bricks every time." The usual statistics certainly support the fact that Stoke have indeed changed their approach. Whoscored tells us Stoke's possession is up over 6% so that they're now basically sharing the ball 50-50 with opponents. Pass accuracy is up nearly 10%, they've played an average of 100 MORE short passes per game than last year and 9 long balls less. And all this having played Liverpool, Man City and Arsenal already. I though there was a pretty good chance that a lot of this passing might be guff. Lots of sideways passing between centre halves, lot's of middle third possession not actually hurting the opposition. I'll be honest, I haven't seen more than a minute of Stoke's games this season. I'm not sure if I've even seen all their goals yet. What i'm trying to say here is that I simply don't know. What I wanted to do, and what I have done then, is to take a look at the types of chances Stoke created last season and compare them to the types of chances they've created this. On a spreadsheet. Still here? Good. For your viewing pleasure I have translated it all into some easily understood (hopefully) graphics.

For future reference, the 'wheat' zone is the central area inside the box where the vast majority of goals are scored from. See here for further reading. Going on these images it seems the types/number of chance Stoke have created in the wheat zone this season are almost identical to last season's. They've just increased the number of shots they've taken from the 'chaff' areas outside the box. However, when we take into account the opposition played so far, Stoke's passing where it matters HAS improved. My chance creation model enables an expected goals figure to be calculated based on type and number of chances made. In the exact same fixtures last season (substitute Reading for Palace in the newly promoted bottom of the table team stakes) Stoke could have expected to score 3.6 goals in the 6 games. This season they could have expected to score just over 7 goals. For now it doesn't matter that Stoke have only actually converted 4 of these chances. If they carry on creating these chances, they could well see a steady improvement on last year. Follow me on Twitter here.

By Ted Knutson Greetings, and welcome to another edition of our Predictive Models, this time featuring Italy’s top league. If you aren’t familiar with what these are or why you might care, feel free to click here [LINK] for a more complete explanation. For the rest of you, let’s dive right in.

League Winner: Juventus, Roma Champions League Spots: AC Milan, Inter, Lazio, Napoli Relegation Battle: Uh, it's complicated. This is not what I expected at all. Roma are at the top of the rankings (they were likely projected around 6^th preseason), and Fiorentina are struggling down in 7^th (having lost Super Mario Gomez very early on). Both Milan teams are playing better than I figured (Inter in both coefficient and results, Milan in co-efficient only) and… Lazio? Lazio are rated above Napoli? LAZIO?!? The same Lazio who have already played at Juventus and at Roma? This is deeply unexpected. (For me, anyway. You guys probably already knew all this.) This is the same team that I said just yesterday, “don’t count as having played anybody.” I need to apologize to Lazio and their fans. I was wrong. I'm sorry. You guys are totally anybodies. While talking about strength of schedule, I can say that Roma have had it fairly easy thus far, while Inter have played both Juve and Fiorentina and done quite well. I didn’t think Mazarri would be able to whip them into shape this quickly, especially with all the defensive issues they suffered last year, but that’s exactly what has happened. The short of it is: there are six strong teams at the top of Serie A right now, with Roma and Juve currently a class above the rest. In fact, their coefficients through the first six matches are in the same range as Dortmund and Bayern Munich, meaning they have been hugely dominant. Fiorentina could potentially join the teams at the top, but they need more defensive solidity and better health. At the bottom end of the model expectation, strength of schedule is wreaking havoc. Verona and Bologna (very hard opening schedule) are the “worst of the lot”, with Catania (very hard opening schedule), Chievo (very hard opening schedule), Sassuolo (yep, Inter, Napoli, Lazio), and Atalanta at the edge of the danger zone. (All of these teams have production that is quite a bit worse than the rest of the league). Sampdoria are probably only hanging out down there because they too have faced Juventus, Roma, and Milan in the first six matches – I think they are a lot better than this. Yes, I know Verona have 10 points, despite giving up 20.5 shots and 8 shots on target per game. No, I can’t quite explain how that has happened except to note they also had a miserable opening schedule (Juve, Roma, Milan). Variance is a bitch. Sassuolo, despite some abusive beatings, have fundamentals that suggest they should get a bit better as they go along as well. So regarding the bottom of the table, take all of this with a grain of salt. Usually there's more balance to the schedule overall and we can draw at least some vague conclusions at this point. Not this year. Basically every single team down there has faced two or three of the six best teams in the league in their first six matches and been roughed up. The next five matches will be a much better indicator of where these guys will end up at the end of the year.