20141115 - Minor text edits to GoldMining Markdown

RMarkdown/GoldMining/GoldMining.Rmd

@@ -46,7 +46,7 @@ htests[order(-std_pm),std_rank:=1:.N]
 ffa[,c("std_ave_fpts","ppr_ave_fpts"):=list(mean(std_fpts),mean(ppr_fpts)),by=name]
 premium<-ffa[,list(name,writer,premium=std_fpts-std_ave_fpts)]
 wpremium<-BreakBuild(premium,BCol = "writer",IDCols = "name",ValCols = "premium")
-writers<-list(ffs="Fantasy Football Sharks",jamey_eisenberg="CBS's Jamey Eisenberg", dave_richard="CBS's Dave Richard",espn="ESPN",pp="Picking Pros",yahoo="Yahoo Sports",fx="Fox Sports",fft="Fantasy Football Today")
+writers<-list(ffs="Fantasy Football Sharks",jamey_eisenberg="CBS\'s Jamey Eisenberg", dave_richard="CBS\'s Dave Richard",espn="ESPN",pp="Picking Pros",yahoo="Yahoo Sports",fx="Fox Sports",fft="Fantasy Football Today")
 wpremium
 
 htests[,std_upside:=std_pm_h-std_pm]
@@ -68,9 +68,9 @@ The graph below summarizes the projections from a variety of sources.  This week
 
 From this graph be sure to notice:
 
- - `r p_and(htests[order(-std_upside)][1:5][order(std_ave_rank)][,unique(name)])` are the five players with the <b>largest upside</b> (as measured from their (pseudo)medians).  For these players, some projections are placing much higher valuations than others. If you are projected to lose this week by quite a few points and are looking for a risky play that may tip the balance in your favor, these are players to consider.
- - `r p_and(htests[order(std_downside)][1:5][order(std_ave_rank)][,unique(name)])` are the players with the <b> smallest downside</b>, which suggests that while their median projection might not be great, there is less uncertainty concerning how poorly they may perform.  So, if your are likely to win by a lot and want to reduce your downside risk, these players may deserve extra attention.
- - On the other hand, `r p_and(htests[order(-std_downside)][1:5][order(std_ave_rank)][,unique(name)])`  are the five players with the <b>largest downside</b> this week.  If you are planning on starting them, it may be prudent to investigate why some projections have such low expectations for these players.
+ - `r p_and(htests[order(-std_upside)][1:5][order(std_ave_rank)][,unique(name)])` are the five players with the <b>largest upside</b> (as measured from their pseudo-medians).  For these players, some projections are placing much higher valuations than others. If you are projected to lose this week by quite a few points and are looking for a risky play that may tip the balance in your favor, these are players to consider.
+ - `r p_and(htests[order(std_downside)][1:5][order(std_ave_rank)][,unique(name)])` are the players with the <b> smallest downside</b>, which suggests that while their median projection might not be great, there is less uncertainty concerning how poorly they may perform.  So, if you are projected to win by a lot and want to reduce your downside risk, these players may deserve extra attention.
+ - On the other hand, `r p_and(htests[order(-std_downside)][1:5][order(std_ave_rank)][,unique(name)])` are the five players with the <b>largest downside</b> this week.  If you are planning on starting them, it may be prudent to investigate why some projections have such low expectations for these players.
 
 <center> 
 ```{r,echo=FALSE,fig.height=8,fig.width=8}
@@ -107,8 +107,8 @@ ggplot(htests, aes(x=std_pm, y=std_rank, color=factor(std_tier))) +
 
 From this graph be sure to notice:
 
- - `r p_and(htests[order(-ppr_upside)][1:5][order(ppr_ave_rank)][,unique(name)])` are the five players with the <b>largest upside</b> (as measured from their (pseudo)medians).  For these players, some projections are placing much higher valuations than others. If you are projected to lose this week by quite a few points and are looking for a risky play that may tip the balance in your favor, these are players to consider.
- - `r p_and(htests[order(ppr_downside)][1:5][order(ppr_ave_rank)][,unique(name)])` are the players with the <b> smallest downside</b>, which suggests that while their median projection might not be great, there is less uncertainty concerning how poorly they may perform.  So, if your are likely to win by a lot and want to reduce your downside risk, these players may deserve extra attention.
+ - `r p_and(htests[order(-ppr_upside)][1:5][order(ppr_ave_rank)][,unique(name)])` are the five players with the <b>largest upside</b> (as measured from their pseudo-medians).  For these players, some projections are placing much higher valuations than others. If you are projected to lose this week by quite a few points and are looking for a risky play that may tip the balance in your favor, these are players to consider.
+ - `r p_and(htests[order(ppr_downside)][1:5][order(ppr_ave_rank)][,unique(name)])` are the players with the <b> smallest downside</b>, which suggests that while their median projection might not be great, there is less uncertainty concerning how poorly they may perform.  So, if you are likely to win by a lot and want to reduce your downside risk, these players may deserve extra attention.
  - On the other hand, `r p_and(htests[order(-ppr_downside)][1:5][order(ppr_ave_rank)][,unique(name)])` are the five players with the <b>largest downside</b> this week.  If you are planning on starting them, it may be prudent to investigate why some projections have such low expectations for these players.
 
 <center>