Predictions Vol.1 (“Prediction of the FIFA World Cup 2018”)

The below rough excerpt of Prediction of the FIFA World Cup 2018 – A random forest approach with an emphasis on estimated team ability parameters (PDF; 06/08/2018) | Andreas Groll, Christophe Ley, Gunther Schauberger, Hans Van Eetvelde is on our own.

p.1: …four previous FIFA World Cups 2002 – 2014: Poisson regression models, random forests and ranking methods. …
p.2: … By aggregating the winning odds from several online bookmakers and transforming those into winning probabilities, inverse tournament simulation can be used to compute team-specific abilities… With the team-specific abilities all single matches are simulated via paired comparisons and, hence, the complete tournament course is obtained. Using this approach, Zeileis, Leitner, and Hornik (2018) forecast Brazil to win the FIFA World Cup 2018 with a probability of 16.6%, followed by Germany (15.8%) and Spain (12.5%).
…(Audran, Bolliger, Kolb, Mariscal, and Pilloud, 2018): they obtain Germany as top favorite with a winning probability of 24.0%, followed by Brazil (19.8%) and Spain (16.1%). They use a statistical model based on four factors that are supposed to indicate how well a team will be doing during the tournament: the Elo rating, the teams’ performances in the qualifications preceding the World Cup, the teams’ success in previous World Cup tournaments and a home advantage. …
p.5:
Economic Factors [GDP per capita, Population],
Sportive factors [ODDSET probability, FIFA rank],
Home advantage [Host, Continent, Confederation],
Factors describing the team’s structure [(Second) maximum number of teammates, Average age, Number of Champions League (Europa League) players, Number of players abroad/Legionnaires],
Factors describing the team’s coach
p.8: 3.1 Random forests
…an aggregation of a (large) number of classification or regression trees (CARTs). …to find partitions such that the respective response values are very homogeneous within a partition but very heterogeneous between partitions. CARTs can be used both for metric response (regression trees) and for nominal/ordinal responses (classification trees). The most frequent visualization tool for CARTs is the so-called dendrogram…
p.11: 3.2 Regression
…the scores of the competing teams are treated as (conditionally) independent variables following a Poisson distribution (conditioned on certain covariates)…
p.13: 3.3 Ranking methods
…how Poisson models can be used to lead to rankings that reflect a team’s current ability… The main idea consists in assigning a strength parameter to every team and in estimating those parameters over a period of M matches via weighted maximum likelihood, where the weights are of two types: time depreciation and match importance…
… The match importance weights are directly inherited from the official FIFA ranking and can take the values 1 for a friendly game, 2.5 for a confederation or world cup qualifier, 3 for a confederation tournament…, and 4 for World Cup matches. …
p.15: 3.4 Combining methods
1. Form a training data set containing three out of four World Cups.
5. Compare predicted and real outcomes for all prediction methods.
p.16: …three different performance measures to compare the predictive power of the methods:
…the multinomial likelihood, the classification rate, the rank probability score (RPS)…

p.20: 4 Prediction of the FIFA World Cup 2018
…combination of a random forest with adequate team ability estimates from a ranking method… The abilities were estimated by the bivariate
Poisson model with a half period of 3 years. All matches of the 228 national teams played since 2010-06-13 up to 2018-06-06 are used for the estimation, what results in a total of more than 7000 matches. All further predictor variables are taken as the latest values shortly before the World Cup…
4.1 Probabilities for FIFA World Cup 2018 Winner
…according to our random forest model, Spain is the favored team with a predicted winning probability of 17.8% followed by Germany, Brazil,
France and Belgium. … While Oddset favors Germany and Brazil, the random forest model predicts a slight advantage for Spain. …
p.21: Table 8: Estimated probabilities (in %) for reaching the different stages in the FIFA World Cup 2018 for all 32 teams based on 100,000 simulation runs of the FIFA World Cup together with winning probabilities based on the ODDSET odds.
p.22: Figure 4: Winning probabilities conditional on reaching the single stages of the tournament for the five favored teams.
p.23: 4.2 Most probable tournament course
… While in Group B and Group G the model forecasts Spain followed by Portugal as well as Belgium followed by England with rather high probabilities of 38.5% and 38.1%, respectively, other groups such as Group A, Group F and Group H seem to be more volatile. …
According to the most probable tournament course, instead of the Spanish the German team would win the World Cup. However, again it becomes obvious
that with (in that case) Switzerland the German team has to face a much stronger opponent than Spain in the round-of-sixteen. Even though still being the favorite in this match, they would succeed to move on to the quarter finals only with a probability of 61%. While in the most probable course of the knock-out stage, though having tough times in all single stages, Germany would make its way into the final and defend the title…
p.24: Table 9: Most probable final group standings together with the corresponding probabilities for the FIFA World Cup 2018 based on 100,000 simulation runs.
5 Concluding remarks
random forests, Poisson regression models and ranking methods. The former two approaches incorporate covariate information of the opposing teams, while the latter method pro-
p.25: Figure 5: Most probable course of the knockout stage together with corresponding probabilities for the FIFA World Cup 2018 based on 100,000 simulation runs.
vides team ability parameters which serve as adequate estimates of the current team strengths. …by incorporating the team ability parameters from the ranking methods as an additional covariate into the random forest the predictive power becomes substantially increased, leading to the best model capable of beating the bookmakers. …
p.26: …the fact that overall Spain is slightly favored over Germany is mainly due to the fact that Germany has a comparatively high chance to drop out in the round-of-sixteen. Actually, conditioned that Germany reaches the quarter finals, it overtakes Spain…


Virginia Vol.4

Virginia1
Virginia2
Virginia3
The Commonwealth of Virginia
City of Virginia Beach
City of Norfolk
City of Chesapeake
City of Richmond
City of Newport News
City of Alexandria
City of Hampton


https://twitter.com/HiltonNewsroom/status/1004037769805459458


Virginia Vol.3


https://twitter.com/rmeese/status/1002527545261264896


Virginia Vol.2


https://twitter.com/urichmond/status/1008001300087394304


https://twitter.com/UMaryWash/status/1007314949721460736


https://twitter.com/longwoodu/status/1004522041352114176


https://twitter.com/VSUTrojans/status/1006896826161729536


US Policy Changes Vol.92 (Quantitative Easing)

(The below excerpt is on our own.)

The Fed’s Yield-Curve-Control Policy (w PDF; 11/29/16) | Owen F. Humpage @ClevelandFed

…some may follow the example of Japan, which recently added a new long-term interest-rate target to its short-term target to give itself “yield-curve control.” …combining yield-curve control with quantitative easing when government borrowing needs are substantial can create constraints on monetary policy that are not easily removed.

… The Bank of Japan currently sets its short-term policy target—a rate paid on bank reserves—at –0.1 percent and now promises to cap its long-term target rate—that on 10-year government bonds—at approximately zero for the time being. …

The ultimate objective of recent quantitative easing programs in Japan, the United States, and elsewhere has been to lower long-term interest rates when policy rates are at their effective lower bound. …

… The Fed’s experience, however, suggests that combining yield-curve control with quantitative easing when government borrowing needs are substantial can create constraints on monetary policy that are not easily removed. Moreover, a central bank’s heavy involvement in a market can distort the behavior of private market participants to the detriment of market efficiency.

Wartime Yield-Curve Control
… The economy had been recovering from the 1937–38 recession, and by late 1941, output had caught up to where it likely would have been had the Great Depression never occurred. Likewise, the unemployment rate fell sharply in 1941, but some slack remained in the labor market at the start of 1942. …

… Gold had generally been flowing into the United States since Franklin D. Roosevelt devalued the dollar in 1934. …commercial banks held record levels of excess reserves in 1940, and the Federal Open Market Committee (FOMC) fretted that reserves “had risen beyond the System’s power to restrain an inflationary credit expansion should one develop” …

Figure 1.

In early 1942, … the Fed agreed to peg the Treasury-bill yield at 0.375 percent, to cap the critical long-term government bond yield at 2.5 percent… Setting interest rates in this manner, however, allowed the Treasury to expand bank reserves by issuing more securities than the public wished to hold when yields reached their caps, because the Fed then had to purchase them. …

…no one really knew if this yield-curve-control policy would work. Treasury Secretary Henry Morgenthau, the only official with authority to announce the program to the public, never did so. He seemed to prefer a quantitative (excess-reserves) target. …

To tamp down measured inflation and inflation expectations, the Roosevelt Administration began introducing limited price controls as early as May 1940… The controls remained in place until November 1946…

… Excess reserves… continued to fall through mid-1944.

Under its yield-curve-control program, the Fed bought $20 billion worth of Treasury securities or approximately 10 percent of the debt that the Treasury issued between March 1942 and August 1945…

Figure 2.

The Accord
…bond holders would liquidate their government securities, forcing the Fed under its yield-curve-control policy to create reserves… By 1945, banks’ holdings of government securities equaled more than half of their total assets, and a substantial proportion of these matured beyond five years…

… The Treasury believed that it could not possibly finance its unprecedented levels of public debt at reasonable interest rates without the Fed’s continued participation in the government-securities market…

… In 1950, an inflation scare associated with the Korean War and growing congressional support for the Fed’s position led to a Treasury–Fed accord in March 1951, largely freeing monetary policy from its subordinate status vis-à-vis the Treasury’s debt-management operations…

Collateral Damage
… Moreover, the FOMC had directed its trading desk to maintain orderly conditions in government securities markets…

Federal Reserve Chairman William McChesney Martin… feared… by creating a “disconcerting degree of uncertainty” about when, how much, and where on the yield curve the Fed might intervene…

Martin believed that the Fed’s frequent interventions in the longer-term government securities market during and immediately after the war had robbed the market of its “depth, breadth, and resiliency,” … Martin wanted to confine open market operations to the short-end of the yield curve—preferably Treasury bills. …

… Many economists observed that the connection between changes in short-term rates and long-term rates—the link “bills preferable” relied on—was generally weak and often not dependable. They contended that open market operations in longer-term securities offered a viable mechanism for affecting the yield curve’s shape, which was as important as the level of interest rates. …

… Nevertheless, the Fed remained wary of the political consequences of appearing to interfere with the Treasury’s debt-financing operations and from 1954 through mid-1975 engaged in “even keel” operations. …

Passé?
… Yield-curve control gave the Treasury substantial influence over monetary policy and highlighted the major effect that monetary policies had on the cost of financing the government’s huge debts.

… The Bank of Japan has greatly expanded its portfolio of government bonds and has become a major factor in the government bond market, much like the Fed in 1945. …


New Hampshire Vol.2

NewHampshire1NewHampshire2


West Virginia Vol.2

WestVirginia1
WestVirginia2


US Policy Changes Vol.91 (North Korea Vol.8)


https://twitter.com/MFA_Ukraine/status/975362683745787905


Arkansas Vol.2

Arkansas1
Arkansas2
@arkansasgov
@CityLittleRock
City of Fort Smith blog
City of Fayetteville GIS Interactive Maps
City of Springdale GIS & Interactive Mapping
@CityofJonesboro


US Policy Changes Vol.90 (Trade Vol.7)


https://twitter.com/BrookingsInst/status/1005571994031742976


UK Vol.114 (Post-EUref #Brexit Vol.38)


https://twitter.com/tepsaeu/status/973476018630557696
https://twitter.com/EU_Commission/status/973471445669081089


US Policy Changes Vol.89 (North Korea Vol.7: US-North Korea summit meeting)


https://twitter.com/France24_fr/status/1006469024845303810


US Policy Changes Vol.88 (North Korea Vol.6: US-North Korea summit meeting)


US Policy Changes Vol.87 (North Korea Vol.5: US-North Korea summit meeting)


https://twitter.com/BBCNewshour/status/1006521599628140545


US Policy Changes Vol.86 (North Korea Vol.4: US-North Korea summit meeting)


US Policy Changes Vol.85 (North Korea Vol.3: US-North Korea summit meeting)


US Policy Changes Vol.84 (North Korea Vol.2: US-North Korea summit meeting)


US Policy Changes Vol.83 (G7 Canada, Trade, et al.)


https://twitter.com/bbcworldservice/status/1005818970354651138


Tennessee Vol.2

Tennessee1
Tennessee2


https://twitter.com/UTKnoxville/status/993943910232809474


Kentucky Vol.2

Kentucky1
Kentucky2 geo
Kentucky2'' regions


https://twitter.com/MinistryofHemp/status/916063952643039232