{"id":1512,"date":"2019-09-19T20:41:15","date_gmt":"2019-09-19T19:41:15","guid":{"rendered":"https:\/\/marriott-stats.com\/nigels-blog\/?p=1512"},"modified":"2019-11-04T08:22:33","modified_gmt":"2019-11-04T08:22:33","slug":"rugby-world-cup-who-will-win-in-2019","status":"publish","type":"post","link":"https:\/\/marriott-stats.com\/nigels-blog\/rugby-world-cup-who-will-win-in-2019\/","title":{"rendered":"Rugby World Cup #1 &#8211; Who will win in 2019? &#8211; Pool Stage"},"content":{"rendered":"<p>Augustin Pichot, vice-president of World Rugby, <a href=\"https:\/\/twitter.com\/MarriottNigel\/status\/1167379861561905154\" target=\"_blank\" rel=\"noopener noreferrer\">may not like its ranking system<\/a> but ahead of the 2019 Rugby World Cup (Men&#8217;s), I have been able to use rankings to correctly predict 80% of international matches since 2017.\u00a0 For the world cup that starts this week, I used a dynamic ranking model to predict all matches and it shows that 4 countries cannot be separated for the trophy so we are in for some very exciting matches!<\/p>\n<p><!--more-->As of today, <a href=\"https:\/\/www.world.rugby\/rankings\/mru\" target=\"_blank\" rel=\"noopener noreferrer\">World&#8217;s Rugby Rankings for the 20 teams<\/a> taking part in the 2019 World Cup are as listed in order here.\u00a0 Since Japan is the host nation, 3<img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-1994\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-1-124x300.png\" alt=\"\" width=\"192\" height=\"465\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-1-124x300.png 124w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-1-144x350.png 144w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-1.png 308w\" sizes=\"auto, (max-width: 192px) 100vw, 192px\" \/> points have been added to their ranking points which puts them level with France rather than Argentina.\u00a0 It should be noted that Namibia are in fact in 23rd place in the full rankings but 3 teams ahead of them (Spain, Romania &amp; Portugal) failed to qualify.<\/p>\n<p>I have noticed a few people using this list to make predictions of all the matches but they forget that after each match the rankings will be updated.\u00a0 Instead, a dynamic ranking model is needed to take into account changes in ranking points as matches are completed.\u00a0 <a href=\"https:\/\/www.world.rugby\/rankings\/explanation\" target=\"_blank\" rel=\"noopener noreferrer\">World Rugby explain in detail how this is done<\/a> so I will just list the key points.<\/p>\n<ul>\n<li>Ranking points work on a point exchange basis.\u00a0 If team A beats team B and gains X points then team B will lose X points as well.<\/li>\n<li>The maximum number of points that can be exchanged is 4 points since it is a world cup.\u00a0 Outside a world cup the maximum is 2 points.<\/li>\n<li>If a team wins by 15 or more points then the points exchanged is multiplied by 1.5.\u00a0 So in fact the maximum that can be exchanged is 6 points.<\/li>\n<li>The difference in ranking points between the two teams is called the <strong>ranking gap<\/strong>.<\/li>\n<li>If the ranking gap exceeds 10 points, then the stronger team cannot gain any points from winning but will lose 2 points for a draw and 4 points for a defeat with the weaker team gaining these points.<\/li>\n<li>If the ranking gap is zero i.e. both teams are equally matched, the winner gains 2 ranking points and the loser loses 2 ranking points.<\/li>\n<li>Otherwise where the ranking gap is less than 10 points, the stronger team will gain fewer points from winning than the weaker team would have gained from winning but the sum of the two possibilities will always be 4 points e.g. a stronger team can gain 1 point from whilst a weaker team can gain 3 points from winning.<\/li>\n<\/ul>\n<h4><span style=\"color: #008000\"><strong>The HighRank Model<\/strong><\/span><\/h4>\n<p>So let&#8217;s illustrate with two games from the first round.<\/p>\n<p>Japan will open the tournament against Russia.\u00a0 Russia has 64.8 ranking points and Japan 79.7 points so the ranking gap is 14.9 points.\u00a0 Since this is greater than 10 points, Japan will gain nothing and Russia will lose nothing if Japan wins.\u00a0 However, if Russia shock Japan, Russia will gain 4 points if they win by less than 15 points and gain 6 points if they win by 15 points or more.\u00a0 Japan would lose 4 or 6 points accordingly.<\/p>\n<p>A much closer game will be New Zealand against South Africa.\u00a0 The ranking gap is 2.1 points and the formula works out that if New Zealand win, they will gain 1.6 points and South Africa will lose 1.6 points.\u00a0 But if South Africa wins, they will gain 2.4 points whilst New Zealand will lose 2.4 points.\u00a0 The imbalance is because New Zealand are currently ahead of South Africa in the ranking table.\u00a0 Should South Africa win, then by the start of the next round of matches, South Africa will have overtaken New Zealand in the ranking table.\u00a0 This is the dynamic ranking model I have talked about at work.<\/p>\n<p>My HIGHRANK model says that the winner of each match will be the country with the higher ranking points.<\/p>\n<h4><span style=\"color: #008000\"><strong>The ExpWin Model<\/strong><\/span><\/h4>\n<p>My experience of using rankings since 2017 has allowed me to test the historical accuracy of rankings.\u00a0 Overall, 81% (69 of 85) matches have had the winner correctly predicted but this varies by ranking gap:-<\/p>\n<ul>\n<li>For gaps less than 3 points, 12 out 20 or 60% were correct.<\/li>\n<li>For gaps 3 to 6 points, 20 out of 25 or 80% were correct.<\/li>\n<li>For gaps 7 to 13 points, 20 out of 23 or 87% were correct.<\/li>\n<li>For gaps 14 points or larger, 17 out of 17 or 100% were correct.<\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1989 alignleft\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-4-300x250.png\" alt=\"\" width=\"386\" height=\"322\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-4-300x250.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-4-419x350.png 419w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-4.png 751w\" sizes=\"auto, (max-width: 386px) 100vw, 386px\" \/>What is particularly interesting is that the ranking gap is predictive of the score gap i.e. by how many points did the stronger team beat the weaker team by?\u00a0 Note the score gap is strictly Stronger Team Score minus Weaker Team Score so if the score gap is negative, it means the stronger team lost the match.\u00a0 The chart here shows the relationship for 5 different schedules since 2017 and the general relationship is quite consistent but obviously with a large margin of error.\u00a0 The final model I have built using this data is<\/p>\n<p style=\"padding-left: 40px\"><span style=\"color: #993300\"><strong>Expected Score Gap = Ranking Gap x 1.75 &#8211; 1.58, <\/strong>standard error 13.7 points with residuals following a normal distribution<strong>.<\/strong><\/span><\/p>\n<p>Since the errors are normally distributed, I can also calculate the probability of a stronger team winning based on a normal distribution.\u00a0 For example, France play Argentina in the first round where the ranking gap is 3.4 points.\u00a0 Using the formula above, the expected Score Gap is 4.4 points which means France are expected to beat Argentina by 4.4 points.\u00a0 But the standard deviation of the errors in this prediction is +\/-13.7 points which indicates a very large margin of error.\u00a0 You can use Microsoft Excel to work out that there is a 37% chance of the score gap being less than zero i.e. Argentina win, which means that there is a 63% chance of France winning.\u00a0 The formula to use is <strong>=NORMDIST(0, 4.4, 13.7, 1)<\/strong>.\u00a0 Later on, I will show the probability of the stronger team winning using a column headed <strong>P(Win)<\/strong>.<\/p>\n<p>You will note I am not considering draws here.\u00a0 I could have defined a defeat for the stronger team to be when the score gap is less than -0.5 and a win for the stronger team to be when the score gap is greater than +0.5 and a draw to be when the expected score gap is between -0.5 &amp; +0.5.\u00a0 I&#8217;ve chosen not to do this.<\/p>\n<p>If you look closely at my Score Gap model, you can see that if the ranking gap is very small say 0.5, then the expected score gap will be negative.\u00a0 In other words, the slightly weaker team is more likely to win.\u00a0 This indicates that World Rugby&#8217;s rankings are slightly miscalibrated.\u00a0 Should this happen, then my EXPWIN model will predict the weaker team to win, a different prediction from the HIGHRANK model.<\/p>\n<h4><span style=\"color: #008000\"><strong>Pool A &#8211; Predictions<\/strong><\/span><\/h4>\n<p>So let&#8217;s get on with the world cup!\u00a0 Here are my predictions for Pool A using dynamic ranking and my HIGHRANK and EXPWIN models.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1990 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5A-300x145.png\" alt=\"\" width=\"645\" height=\"312\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5A-300x145.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5A-768x372.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5A-450x218.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5A.png 975w\" sizes=\"auto, (max-width: 645px) 100vw, 645px\" \/><\/p>\n<p>Pool A looks like it will head for a fantastic climax.\u00a0 Ireland are favourites to win all their games but second place will come down to a battle between Scotland and hosts Japan in the very last match.\u00a0 Notice how Scotland start out with more ranking points than Japan but by the time the last match is played, the dynamic ranking model closes that to under 1 point and is just enough to give Scotland a 49.9% chance of beating Japan using my Expected Score Gap model.\u00a0 So EXPWIN predicts a win for Japan even though HIGHRANK favours Scotland.<\/p>\n<p>I am using EXPWIN as my preferred model at the moment so this puts Japan into the quarter finals as runner up.\u00a0 But it is worth noting that I could have used a different model.\u00a0 If you sum up the P(Win) column for all countries, with losers getting 100% minus the displayed P(Win) for winners, we end up with the expected number of wins for each country.\u00a0 It turns out that Scotland are expected to win 2.5 matches whilst Japan are expected to win 2.4 matches.\u00a0 In effect it is neck and neck here.<\/p>\n<p><strong>Pool B &#8211; Predictions<\/strong><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1991 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5B-300x145.png\" alt=\"\" width=\"643\" height=\"311\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5B-300x145.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5B-768x372.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5B-450x218.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5B.png 975w\" sizes=\"auto, (max-width: 643px) 100vw, 643px\" \/><\/p>\n<p>Both my models show that the pool will be decided by the first match, New Zealand V South Africa.\u00a0 The win probabilities split 56:44 so it should be a great match!<\/p>\n<h4><span style=\"color: #008000\"><strong>Pool C &#8211; Predictions<\/strong><\/span><\/h4>\n<p>My team England are expected to win the group though the last game against France is by no means a walkover.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1992 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5C-300x145.png\" alt=\"\" width=\"641\" height=\"310\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5C-300x145.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5C-768x372.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5C-450x218.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5C.png 975w\" sizes=\"auto, (max-width: 641px) 100vw, 641px\" \/><\/p>\n<h4><strong><span style=\"color: #008000\">Pool D &#8211; Predictions<\/span><\/strong><\/h4>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1993 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5D-300x145.png\" alt=\"\" width=\"641\" height=\"310\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5D-300x145.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5D-768x372.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5D-450x218.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-5D.png 975w\" sizes=\"auto, (max-width: 641px) 100vw, 641px\" \/><\/p>\n<p>Wales are expected to win the group but it is worth keeping an eye on the Australia V Fiji match where Fiji could catch Australia cold.<\/p>\n<h4><span style=\"color: #008000\"><strong>Expected Quarter Finalists<\/strong><\/span><\/h4>\n<p>The full list of expected positions in each pool is below.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1987 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-2-300x95.png\" alt=\"\" width=\"644\" height=\"204\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-2-300x95.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-2-768x243.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-2-1024x324.png 1024w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-2-450x142.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-2.png 1044w\" sizes=\"auto, (max-width: 644px) 100vw, 644px\" \/><\/p>\n<p>The expected number of wins in the last column of each pool is a good guide to the closeness or not of each pool.<\/p>\n<h4><span style=\"color: #008000\"><strong>The Knock Out Rounds<\/strong><\/span><\/h4>\n<p>The dynamic ranking model continues to work away even though we are into the knock outs.\u00a0 The winners of the quarter finals seem straightforward but obviously upsets can occur. In fact, the probability of an upset is 0.67.\u00a0 You can work this out by calculating the probability of all 4 stronger teams winning.\u00a0 This is done by multiplying the 4 P(Win) figures shown and the answer is 0.33<\/p>\n<p>If the 4 pool winners do win their quarter finals, there is no question the semi-finals will be fantastic!<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\" wp-image-1988 alignnone\" src=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-3-300x171.png\" alt=\"\" width=\"647\" height=\"369\" srcset=\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-3-300x171.png 300w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-3-768x439.png 768w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-3-1024x585.png 1024w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-3-450x257.png 450w, https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2019\/09\/WC19-3.png 1043w\" sizes=\"auto, (max-width: 647px) 100vw, 647px\" \/><\/p>\n<p>There is nothing between the 4 semi-finalists with win probabilities no higher than 55%.\u00a0 Anyone can beat anyone.<\/p>\n<p>And then we come to the final!\u00a0 At this point my models diverge with HIGHRANK saying that New Zealand will win a 3rd World Cup in a row and EXPWIN saying Ireland will sneak it.<\/p>\n<p>&nbsp;<\/p>\n<h4><span style=\"color: #008000\"><strong>My articles on the 2019 Rugby World Cup<\/strong><\/span><\/h4>\n<ol>\n<li><a href=\"https:\/\/marriott-stats.com\/nigels-blog\/rugby-world-cup-who-will-win-in-2019\/\" target=\"_blank\" rel=\"noopener noreferrer\">Who will win in 2019 &#8211; Initial predictions ahead of Pool stage<\/a><\/li>\n<li><a href=\"https:\/\/marriott-stats.com\/nigels-blog\/rugby-world-cup-who-will-win-in-2019-2\/\" target=\"_blank\" rel=\"noopener noreferrer\">Who will win in 2019 &#8211; Revised predictions ahead of Knockout stage<\/a><\/li>\n<li><a href=\"https:\/\/marriott-stats.com\/nigels-blog\/rugby-world-cup-who-will-win-in-2019-4\/\" target=\"_blank\" rel=\"noopener noreferrer\">Who will win in 2019 &#8211; Final prediction ahead of the Final<\/a><\/li>\n<li><a href=\"https:\/\/marriott-stats.com\/nigels-blog\/rugby-world-cup-who-will-win-in-2019-3\/\" target=\"_blank\" rel=\"noopener noreferrer\">How accurate were my predictions &#8211; written before the Final<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>Augustin Pichot, vice-president of World Rugby, may not like its ranking system but ahead of the 2019 Rugby World Cup (Men&#8217;s), I have been able to use rankings to correctly predict 80% of international matches since 2017.\u00a0 For the world cup that starts this week, I used a dynamic ranking model to predict all matches [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":1989,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[6,4],"tags":[14,12,134,15,133],"class_list":{"0":"post-1512","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-forecasting","8":"category-sport","9":"tag-forecasts","10":"tag-rugby","11":"tag-rwc2019","12":"tag-sport-analytics","13":"tag-world-cup","14":"entry","15":"override"},"_links":{"self":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1512","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/comments?post=1512"}],"version-history":[{"count":11,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1512\/revisions"}],"predecessor-version":[{"id":2050,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/posts\/1512\/revisions\/2050"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/media\/1989"}],"wp:attachment":[{"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/media?parent=1512"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/categories?post=1512"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/marriott-stats.com\/nigels-blog\/wp-json\/wp\/v2\/tags?post=1512"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}