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THE POWER OF GOALS.
The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is ahandy
THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 toH1001.
THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoringsport!
THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or heractual output.
THE POWER OF GOALS.
The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is ahandy
THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 toH1001.
THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoringsport!
THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or heractual output.
THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoringsport!
THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: HOW TO FRAME AN INDIVIDUAL MATCH OUTCOME. Here's some representative figures. Home teams are scoring 0.25 goals per game more than visitors, 1.49 compared to 1.24. The average game has 1.37 expected goals per team. THE POWER OF GOALS.: LIVERPOOL BY ONE. Liverpool won 10 games by a single goal margin last season. That’s a lot, but well below the single season record held by Manchester United of 16 in 2012/13 and 2008/09. THE POWER OF GOALS.: THE CASE FOR CROSSES. The recently departed Euro Finals provided a paradox for advocates of different styles of play. Spain largely did away with the conventional centre forward, choosing instead to play intricate, short passes in the final third while patiently waiting for an opening to appear. THE POWER OF GOALS.: SCORING EFFICIENCY AND CURRENT SCORE. The tie appeared remarkable for many reasons.Not only had Chelsea played over half the second leg with just ten men and Messi had missed from the spot,but they had also enjoyed less than twenty percent of the possession and had been out shot by a ratio of 3:1over both legs.They had managed just 3 shots compared to Barca's 14 in their 1-0 win at Stamford Bridge and had fared only THE POWER OF GOALS.: OCTOBER 2016 At the dawn of footballing time, managers were lasting on average for around 150 matches, now it's down to about 50. Success rate obviously plays a part in perceived managerial talent and Zenga's so so 47% success rate would typically entitle him to at least a season of honest toil, rather than the 17 matches he was actually granted. THE POWER OF GOALS.: 2011 The first thing to notice is that there is a large variation around the average value of 0.42 goals for the league,Manchester United appear to have a massive preference playing at home compared to on the road,while near neighbours Wigan actually performed better away from their home turf.This quite naturally can lead to the impression that better teams manage to muster larger than averageTHE POWER OF GOALS.
The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is ahandy
THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 toH1001.
THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoringsport!
THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or heractual output.
THE POWER OF GOALS.
The latter, EXPECTED GOALS, is a value ascribed to the quality of attempts on goal, after the fact, based on the characteristics, shot type, location etc of each attempt. The goal expectation of England and Scotland in the upcoming game is around 2.12 goals and 0.48 goals, respectively. The expected goals for the game hasn't yet materialised. THE POWER OF GOALS.: XG AS EASY AS 1,2,3 The table above includes diverse shooting profiles, which may be useful as a descriptor or potential as a coaching aid if the multi-stage xG model can THE POWER OF GOALS.: NON-SHOT XG MODELS This week on the Infogol site, we revealed the work we've been doing to develop a non-shot xG model. The post can be read HERE. NSxG isn't a new concept, the idea's been around in other sports, such as the NFL for decades, but the fluid nature of football/soccer has made such models very data hungry & time consuming to run on a humble works THE POWER OF GOALS.: TWELVE SHOTS GOOD, TWO SHOTS BETTER. To maintain a goal expectation of 1.2 goals for each side, I gave each shot a 10% likelihood of success. So it is an artificial situation, but hopefully a test of the effect of goal expectation being unevenly spread among varying shot quantity. Potentially, team A, based on just two goal attempts can only score a maximum of two goals in a shot THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: CORRELATION AND CAUSATION IN FOOTBALL. Correlation and Causation in Football. When Albert Einstein started work in the Swiss Patent Office his daily mantra was " believe everything is wrong" and that rigorous approach quickly saw him rise to the heady heights of Technical Expert,second class before he took his talents to more demanding fields.A healthy dose of scepticism is ahandy
THE POWER OF GOALS.: RUNNING A SIMPLE SIMULATION WITH EXCEL. Nearly there. Once you've got the cursor flashing in the column input cell, simply click on any cell without data. I've chosen B1001. Click "OK" in the "Data Table" box and the results of 1,000 simulations should with a bit of patience auto-fill into the cells from H3 toH1001.
THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoringsport!
THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: EXPECTED SAVES AGEING CURVE. Everyone is probably familiar with the concept of expected goals, assists and saves by now. A modelled prediction of the likelihood that a player will score, based mainly on the location and type of attempt is summed over a number of attempts and then compared to his or heractual output.
THE POWER OF GOALS.: HOW PASSING SEQUENCES CREATE CHANCES. Based on the data and the individual pass expectations, Bolton had about a 1% chance of completing such a move and the last four passes were among the sequence's most difficult attempts. Eagles had around a 10% chance of scoring with his effort. Once we put all these numbers together it quickly becomes apparent why football is a low scoringsport!
THE POWER OF GOALS.: QUANTIFYING THE VALUE OF EVERY PASS This represents the starting point of every successful pass. The plot is best used in conjunction with video analysis, but you can quickly see that Rice's sphere of influence is concentrated broadly in front of the back four and across the line, but he also delivers an impressive range of threatening passing options mid way inside the opposition half and just leftfield. THE POWER OF GOALS.: USING EXCEL TO SIMULATE VILLA'S DEMISE. Now we need the data table/What if to run the simulation, in this case 1,000 times. count column L up from 1 to 1,000 and paste K16, the total points won by Villa from our projected odds into M1. Select M1000 to L1. Click "What if", then Data Table, then THE POWER OF GOALS.: BIG CHANCE OR NO BIG CHANCE. There has been a fair bit of comment recently around big chances and their inclusion or not in shot based expected goals models. Big chances are, as the name suggests, a partly subjective addition to the Opta data feed which describes a goal attempt. THE POWER OF GOALS.: HOW TO FRAME AN INDIVIDUAL MATCH OUTCOME. Here's some representative figures. Home teams are scoring 0.25 goals per game more than visitors, 1.49 compared to 1.24. The average game has 1.37 expected goals per team. THE POWER OF GOALS.: LIVERPOOL BY ONE. Liverpool won 10 games by a single goal margin last season. That’s a lot, but well below the single season record held by Manchester United of 16 in 2012/13 and 2008/09. THE POWER OF GOALS.: THE CASE FOR CROSSES. The recently departed Euro Finals provided a paradox for advocates of different styles of play. Spain largely did away with the conventional centre forward, choosing instead to play intricate, short passes in the final third while patiently waiting for an opening to appear. THE POWER OF GOALS.: SCORING EFFICIENCY AND CURRENT SCORE. The tie appeared remarkable for many reasons.Not only had Chelsea played over half the second leg with just ten men and Messi had missed from the spot,but they had also enjoyed less than twenty percent of the possession and had been out shot by a ratio of 3:1over both legs.They had managed just 3 shots compared to Barca's 14 in their 1-0 win at Stamford Bridge and had fared only THE POWER OF GOALS.: OCTOBER 2016 At the dawn of footballing time, managers were lasting on average for around 150 matches, now it's down to about 50. Success rate obviously plays a part in perceived managerial talent and Zenga's so so 47% success rate would typically entitle him to at least a season of honest toil, rather than the 17 matches he was actually granted. THE POWER OF GOALS.: 2011 The first thing to notice is that there is a large variation around the average value of 0.42 goals for the league,Manchester United appear to have a massive preference playing at home compared to on the road,while near neighbours Wigan actually performed better away from their home turf.This quite naturally can lead to the impression that better teams manage to muster larger than averageTHE POWER OF GOALS.
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WEDNESDAY, 2 OCTOBER 2019 PASSING RISK REWARD IN THE PREMIER LEAGUE The availability of richer data sources has naturally led to an interest in passing and ball progression. The generally quoted passing metrics still gravitate towards event data such as goal attempts and actual scores as the major framework. Passes that lead to a potential goal scoring attempt predominate in most current passing metrics and little has been done to differentiate between the contribution made by individual players involved in thesepossession chains.
In contrast, we've broken down the value of each pass attempted by referencing how likely a possession anywhere on the pitch has historically led to a goal, whether or not the possession ultimately result in an attempt on goal. This so called non shot xG metric not only allows a route to value every ball progression, be it a pass or a carry, but also quantifies individual involvement, rather than sharing the credit equally between all those participating in the possession. However, as often is the case in football metrics, only one side of the ball has been investigated. Each pass attempt comes with a risk and reward. The player attempting the pass has custody of a valuable team resource, namely the non shot xG value for possession of the ball at that precise position on the field. The potential reward in making a progressive pass is to advance the ball to a more dangerous area of the field. And the ever present risk is the cost of a turnover. The passing team lose the NS xG value they had by owning the ball and the opponents gain their own NS xG by taking possession of the ball. Weighing a player's NS xG leger is problematical, but one way to express the risk reward balance of a players passing performance is to add up the NS xG value of every progressive pass they complete and compare this to the sum of the NS xG he loses through incomplete passes, along with the NS xG gained by the opponent taking possession of his errant attempts. For example, in the nascent Premier League, Matteo Guendouzi's completed open play progressive passes have been received at areas on the field that totals 6.69 NS xG. On the minus side, his picked off pass attempts has "lost" Arsenal 1.67 N xG. This is made up of loss of pitch position for Arsenal and the combined NS xG value for the opponent based on where possession iswon.
Overall, and without regard for pass volume or minutes played, Guendouzi has a net positive 5.02 NS xG for Arsenal in 2019/10. This puts him top of the Arsenal "risk/reward" passing charts and we feel is a much better single figure metric to describe a player's involvement in progressing his side towards the opponents goal. Not only does it quantify individual involvement and utilses every pass attempted, it also penalises reckless or sloppy execution that leads to change of possession. Here's the current pass risk/reward numbers for all 20 Premier League players with a minimum number of attempts. Posted by Mark Taylor at 10:120 comments
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SATURDAY, 14 SEPTEMBER 2019 GAME STATE AND BLOCKED SHOTS. I've written a fair bit about game state and how it impacts on how a side approaches a match s the time elapses and occasionally the scoreline changes.
I don't use score differential to define "game state", instead I use a measure of how well each team is fairing based of their pre gameexpectation.
This can be defined as the expected points based on the current score and time elapsed or the expected success rate of a team, again when measured against a pre kick off baseline. The choice is entirely up toyou.
The advantage of this approach is primarily when the game is tied (which it is for a fairly significant portion of most matches). Instead of counting offensive production for_ both_ sides at this score differential, there's usually a clear indication of which of the two teams is happier with the stalemate and which is not. You also get a gradual movement of game state that incorporates the often omitted variable of time elapsed. It's intuitive as to what might happen as game state ebbs and flows over the course of a match, as unhappy teams perhaps become more risk taking in order to change the current status quo, while pregame underdogs are forced or chose to attempt to bank their above expectation gains by becoming more defensive. One slight problem with this approach is that it assumes a relatively balanced competitive edge between competing teams and further assumes that those needing to change the current scoreline are capable of attempting to do so. Not to be harsh, but it's difficult to envisage a situation where Manchester City felt the need to protect a lead against say Newcastle or where Newcastle were technically able to up their attacking intent against the champions. So often the presence of clearly superior teams can skew conclusions. "Possession leads to wins" arose largely because better sides also had high levels of possession, but the possession was a byproduct of other things they did, rather than the primary driver oftheir results.
Remove Barca etc from the data and the relationship between possession and wins tended to disappear. Therefore, firstly here's why "zero goal differential" (the game is level) shouldn't be regarded as a single game state. Here's a sample of matches from the 2018/19 Premier League, involving games where one of the Big 6 wasn't playing. Thus the games weren't particularly one-sided from the outset. Initially, I've simply counted the shot volume from regular play for teams when the score differential is zero (the game is level). The vertical axis records my version of changing game state, a larger negative value indicates that a team that is doing badly compared to the expectation at kickoff. Typically, this may be when a home favourite is level a fair way into the game and a points expectation that may have been 1.75 expected points at 3 o'clock has fallen back towards one point as the clockticks on towards 5.
Those above the blue score differential line of zero are doing better that they hoped for, they might have expected to average less than a point from such a game, but they are edging closer and closer to a point, with a possibility of nicking all three. Each point represents a goal attempt and it's clear that the lions share are being taking by the disgruntled favs. If we re-examine our intuition, it's likely that if the beneficiaries of the stalemate aren't taking that many shots in the match, they're doing things to prevent the ones at the other end going in. Learning from the likes of Pulis and Dyche that will likely includeblocking shots.
Next I built a simple xG model (just location & type), but also included the game state factor, not just at zero goal differential, but at all score differentials to see if it told anything about the likelihood a shot would be blocked or not. I eliminated games where a red card had been shown, for obviousreasons.
The bottom line was that game state was a significant factor in correlating with whether an attempt was blocked or not, along with location and shot type. And the larger the decrease in a side's pre-match expectation when the attempt was taken, the more likely it became that the shot was blocked. In short, without the superstar teams, run of the mill games appear to follow the "hold what we have" and "this is disappointing, let's crackon" mentality.
This is one route to improve the much criticised problem of single xG races, where one team scores early and then drops anchor, but whether it is a universal improvement to a predictive model is a question of over fitting the past and potentially screwing up the future. Posted by Mark Taylor at 11:200 comments
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WEDNESDAY, 11 SEPTEMBER 2019 RUGBY WORLD CUP SIMULATION World Cup's have been like London buses this year and the rugby union version kicks off in a week or so. It's live and complete on terrestrial TV in the UK, with plenty of huge mismatches in the opening group games, before eight teams, (whom could be fairly accurately predicted beforehand) hold the really interesting knockout run to the Webb Ellis Trophy on November 2nd. However, that's not to say that the group matches don't hold any intrigue. There are at least two tier one teams in each of the four groups and while they'll be expected to steamroller the lower grade group opponents, the outcomes of these elite matchup will have a huge bearing on how the pairings for the knockout phase pans out. Therefore, if you want to chart the likelihood of a team's route to the final being paved with Southern hemisphere behemoths, a tournament simulation is the easiest method out there. You'll need a ratings system to kickoff with, assuming you're shunning the merry-go-round that has been the world rankings. Ireland are the current leaders, having recently displaced Wales, who had just displaced New Zealand, who themselves had displaced South Africa....ten years ago. So the world rankings, following a decade of stagnation have suddenlybecome volatile.
Let's make our own, instead. I took the last 20 matches for all participants, and produced an attacking and defensive rating, based around match scores and opponentquality.
New Zealand are the tournament's most potent attack, they'll score around 14 more points against and average team than another average team would manage and Wales, courtesy of rugby league knowhow, has thebest defence.
Next you need a way to simulate game outcomes. The big clash of the group stages sees favourites New Zealand take on South Africa. After matching up the respective attacking and defensive ratings for each team, the model expects the All Blacks to average around 28.5 points and S Africa 23.5. New Zealand are favoured by five points and there's likely to be 52total points.
If we look at the spread of points scored and allowed by each side over the last year or so, we can produce a distribution of points that describes each team's likely scoring pattern in this game. We'll then draw a value randomly from this distribution for each team to simulate a single match scoreline and then repeat the process thousands oftimes.
After adding a few tweaks to mimic the largely redundant bonus points system rugby insists on employing and ensuring that each drawn score from the distributions is a "rugby score" (no scoring a grand total of four points etc), we just repeat for every group game, add up the total points won in the group, follow the draw format and find thewinner.
This is how the simulations shake out. Four sides with a double figure percentage chance of lifting the trophy, New Zealand, S Africa for the south and England and Wales for the north, with the former looking a vulnerable favourite. Posted by Mark Taylor at 10:570 comments
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Rating Rugby Union Kickers By Kick Difficulty Rugby Union, much like football is a fluid, fast moving sport, which requires a large amount of context related data to adequately describe...
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Time of Possession in the EPL. Time of Possession is fast becoming one of the hot topics in the world of football stats and such is it's current pre eminence that it h...*
Expected Saves Ageing Curve. Everyone is probably familiar with the concept of expected goals , assists and saves by now. A modelled prediction of the likelihood that...
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The EPL's Best Keeper. *A season ending version of these number can be found here The most exposed playing position on the football pitch is that ofgoalkeeper.O...
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