‘,p(t,”class”,”first-round-connector svelte-br3pw6″)},m(n,r){W(n,t,r)},d(n){n&&G
top-12 team from our model would be expected to perform in those same games. For example, if a
team’s average margin of victory for the season is 10 points, but the average top-12 team would
be expected to have an average margin of victory of 15 points, their strength of resume would be
-5.0. We also cap the margin of victory from individual games at 50 points when calculating a team’s
season-long average to curb the outsized effect of blowouts and deduct 7.0 points from a team’s
final strength of resume number for each loss – an idea that derives from ESPN’s Bill Connelly
and his Resume+ metric.`,n=N(),r=M(“p”),r.innerHTML=`Strength of Record represents the odds that the average top-12 team from the model
would get that team’s exact record in those same games. For example, if one undefeated team played
a weaker schedule than another undefeated team, the first would have a weaker strength of record.`,o=N(),l=M(“p”),l.textContent=`We blend together each team’s percentile ranking in Strength of Resume and Strength of Record,
which is then fed into another linear regression to project each team’s CFP ranking and set
the bracket. Due to the unpredictability and volatility of human decision making, my algorithm
builds in some “randomness” to each Playoff selection. Add in the fact that the expanded
Playoff could mean that the committee ranks teams slightly different from the past, and we
have plenty of complications to monitor, especially in the first year of a new system.`,s=N(),i=M(“p”),i.textContent=`Once the regular season is over and the bracket is set in each of our 100,000 simulations, we
use our game-by-game projections to simulate which teams reach each round of the College
Football Playoffs and who ultimately wins the championship. We then calculate how often in
these simulations each team reaches certain benchmarks and how many games they win on average.
So, when you see that a team has a 20 percent chance of making the playoffs in the forecast
interactive, for example, that means it made the playoffs in 20 percent of the simulations
we’ve run.`,f=N(),c=M(“p”),c.textContent=`The teams included above either have at least a 0.5 percent chance of making the Playoff, or
are in the top 25 in at least one of the AP poll, coaches poll and the CFP committee rankings.`,u=N(),_=M(“button”),_.textContent=”Show less”,p(t,”class”,”svelte-1m2l0yn”),p(r,”class”,”svelte-1m2l0yn”),p(l,”class”,”svelte-1m2l0yn”),p(i,”class”,”svelte-1m2l0yn”),p(c,”class”,”svelte-1m2l0yn”),p(_,”class”,”read-more svelte-1m2l0yn”)},m(w,v){W(w,t,v),W(w,n,v),W(w,r,v),W(w,o,v),W(w,l,v),W(w,s,v),W(w,i,v),W(w,f,v),W(w,c,v),W(w,u,v),W(w,_,v),g||(m=Pe(_,”click”,e[1]),g=!0)},p:Le,d(w){w&&(G
metrics, such as Expected Points Added and Success Rate. These projections estimate how many
points each team would be expected to score and allow in a game against an average opponent at a
neutral site. We then assign a probability of how likely a team is to win a given game by
adjusting for opponent and location. Taking into account each team’s current record and
remaining schedule, we use these game-by-game projections to simulate the rest of the season
100,000 times.`,l=N(),s=M(“p”),s.textContent=`We also created an algorithm to predict which 12 teams the College Football Playoff committee
would choose. It’s inherently difficult to model the choices of a committee of 12 people, but
we’ve created two quantitative metrics that reflect 10 years of data on the committee’s decision
making process: Strength of Resume and Strength of Record.`,i=N(),C.c(),f=N(),c=M(“h2″),c.textContent=”Credits”,u=N(),_=M(“p”),_.innerHTML=’Reporting: Austin Mock | Editing: Matt Brown, Jill Thaw, Eric Single’,g=N(),m=M(“p”),m.innerHTML=`Design and Development: Ryan Best, Laura Pelton, David Haye, Elliot Jordan,
Oliver Viehweger | Editing: Skye Gould, Marc Mazzoni, Amy Cavenaile`,w=N(),v=M(“p”),v.innerHTML=’Illustration: Dan Goldfarb’,p(n,”class”,”svelte-1m2l0yn”),p(o,”class”,”svelte-1m2l0yn”),p(s,”class”,”svelte-1m2l0yn”),p(c,”class”,”svelte-1m2l0yn”),p(_,”class”,”svelte-1m2l0yn”),p(m,”class”,”svelte-1m2l0yn”),p(v,”class”,”svelte-1m2l0yn”),p(t,”class”,”methodology-wrapper svelte-1m2l0yn”)},m(S,z){W(S,t,z),h(t,n),h(t,r),h(t,o),h(t,l),h(t,s),h(t,i),C.m(t,null),h(t,f),h(t,c),h(t,u),h(t,_),h(t,g),h(t,m),h(t,w),h(t,v)},p(S,[z]){b===(b=k(S))&&C?C.p(S,z):(C.d(1),C=b(S),C&&(C.c(),C.m(t,f)))},i:Le,o:Le,d(S){S&&G
top-12 team from our model would be expected to perform in those same games. For example, if a
team’s average margin of victory for the season is 10 points, but the average top-12 team would
be expected to have an average margin of victory of 15 points, their strength of resume would be
-5.0. We also cap the margin of victory from individual games at 50 points when calculating a team’s
season-long average to curb the outsized effect of blowouts and deduct 7.0 points from a team’s
final strength of resume number for each loss – an idea that derives from ESPN’s Bill Connelly
and his Resume+ metric.`,n=N(),r=M(“p”),r.innerHTML=`Strength of Record represents the odds that the average top-12 team from the model
would get that team’s exact record in those same games. For example, if one undefeated team played
a weaker schedule than another undefeated team, the first would have a weaker strength of record.`,o=N(),l=M(“p”),l.textContent=`We blend together each team’s percentile ranking in Strength of Resume and Strength of Record,
which is then fed into another linear regression to project each team’s CFP ranking and set
the bracket. Due to the unpredictability and volatility of human decision making, my algorithm
builds in some “randomness” to each Playoff selection. Add in the fact that the expanded
Playoff could mean that the committee ranks teams slightly different from the past, and we
have plenty of complications to monitor, especially in the first year of a new system.`,s=N(),i=M(“p”),i.textContent=`Once the regular season is over and the bracket is set in each of our 100,000 simulations, we
use our game-by-game projections to simulate which teams reach each round of the College
Football Playoffs and who ultimately wins the championship. We then calculate how often in
these simulations each team reaches certain benchmarks and how many games they win on average.
So, when you see that a team has a 20 percent chance of making the playoffs in the forecast
interactive, for example, that means it made the playoffs in 20 percent of the simulations
we’ve run.`,f=N(),c=M(“p”),c.textContent=`The teams included above either have at least a 0.5 percent chance of making the Playoff, or
are in the top 25 in at least one of the AP poll, coaches poll and the CFP committee rankings.`,u=N(),_=M(“button”),_.textContent=”Show less”,p(t,”class”,”svelte-1m2l0yn”),p(r,”class”,”svelte-1m2l0yn”),p(l,”class”,”svelte-1m2l0yn”),p(i,”class”,”svelte-1m2l0yn”),p(c,”class”,”svelte-1m2l0yn”),p(_,”class”,”read-more svelte-1m2l0yn”)},m(w,v){W(w,t,v),W(w,n,v),W(w,r,v),W(w,o,v),W(w,l,v),W(w,s,v),W(w,i,v),W(w,f,v),W(w,c,v),W(w,u,v),W(w,_,v),g||(m=Pe(_,”click”,e[1]),g=!0)},p:Le,d(w){w&&(G
metrics, such as Expected Points Added and Success Rate. These projections estimate how many
points each team would be expected to score and allow in a game against an average opponent at a
neutral site. We then assign a probability of how likely a team is to win a given game by
adjusting for opponent and location. Taking into account each team’s current record and
remaining schedule, we use these game-by-game projections to simulate the rest of the season
100,000 times.`,l=N(),s=M(“p”),s.textContent=`We also created an algorithm to predict which 12 teams the College Football Playoff committee
would choose. It’s inherently difficult to model the choices of a committee of 12 people, but
we’ve created two quantitative metrics that reflect 10 years of data on the committee’s decision
making process: Strength of Resume and Strength of Record.`,i=N(),C.c(),f=N(),c=M(“h2″),c.textContent=”Credits”,u=N(),_=M(“p”),_.innerHTML=’Reporting: Austin Mock | Editing: Matt Brown, Jill Thaw, Eric Single’,g=N(),m=M(“p”),m.innerHTML=`Design and Development: Ryan Best, Laura Pelton, David Haye, Elliot Jordan,
Oliver Viehweger | Editing: Skye Gould, Marc Mazzoni, Amy Cavenaile`,w=N(),v=M(“p”),v.innerHTML=’Illustration: Dan Goldfarb’,p(n,”class”,”svelte-1m2l0yn”),p(o,”class”,”svelte-1m2l0yn”),p(s,”class”,”svelte-1m2l0yn”),p(c,”class”,”svelte-1m2l0yn”),p(_,”class”,”svelte-1m2l0yn”),p(m,”class”,”svelte-1m2l0yn”),p(v,”class”,”svelte-1m2l0yn”),p(t,”class”,”methodology-wrapper svelte-1m2l0yn”)},m(S,z){W(S,t,z),h(t,n),h(t,r),h(t,o),h(t,l),h(t,s),h(t,i),C.m(t,null),h(t,f),h(t,c),h(t,u),h(t,_),h(t,g),h(t,m),h(t,w),h(t,v)},p(S,[z]){b===(b=k(S))&&C?C.p(S,z):(C.d(1),C=b(S),C&&(C.c(),C.m(t,f)))},i:Le,o:Le,d(S){S&&G
