dot plot – Coffee Spoons

Revisiting My 2025 Red Sox Predictions

Back in March I posted my predictions for the 2025 Boston Red Sox on my social media feeds. I chose not to post it here, because the images had no real data visualisation and the only real information graphic was my prediction of the playoffs via a bracket. I did, however, write about how the Sox may have found their second baseman of the future in Kristian Campbell.

That prediction looks not so hot. The Sox optioned/demoted Campbell to the minor leagues in June and he has not been seen in Boston since. I still think the kid has long-term potential with his bat, but his globe worries me. It never really came around as I hoped.

But what about all my other predictions for the 2025 Red Sox and baseball more broadly?

Well, since today is Day One of the Postseason, I have nothing to report other than that my prediction of a Red Sox–Phillies World Series wherein Boston wins remains possible. Though the bracket itself, not so much. And as for the awards, we still have to wait and see on those too. The rest of the graphics, which had my predictions for select players and statistics, well, those we can look at here today.

We can start with the divisional standings.

The AL East turned out pretty differently than I thought it would. Baltimore’s young prospects did not have a great year and their rotation was even worse than I thought it would be—and that was not very good, I just thought their young bats would make up the difference.

Similarly, I thought the Yankees would fail to survive some bad injuries to start the year. Unfortunately they did and they host the Red Sox this evening in the Wild Card series.

Toronto finally put it all together after years of failing to do so and Tampa Bay did the opposite, had a bad year where everything fell apart after years of finding a way to put it all together.

In the NL East, the Braves just got hurt and never recovered. But that they were worse than even the Marlins in Miami surprised me. Without Atlanta atop the division, the Phillies and Mets made sense and in that micro-ranking I was spot on.

The West Coast divisions were similarly jumbled. I really thought Texas would dominate, but injuries and poor performances crippled their year. I had thought Houston lost too many big pieces from their roster to remain competitive. And in that I was largely correct as they sputtered out in the final week. As for the Angels and Athletics, the less said about them the better.

The NL West largely went as I expected. The Dodgers did not really dominate the way I and many others expected them to, but they performed well enough to win the division. I thought Arizona, San Diego, and San Francisco would slug it out all year and they largely did, though San Francisco was a bit more disappointing than I thought. And the Rockies were just bad.

As for the two central divisions, what can I say? No notes.

Ultimately I thought Boston would be a better than they were at year’s end. Though they dealt with serious injuries all year and the kids were forced to come up and reinforce the roster a bit too soon then they too all got hurt.

So let us start with the hitters.

The big, glaring, obvious point here is that in the middle of the season, the Boston Red Sox traded away their best player, at least their best hitter, in Rafael Devers. I thought then it was a stupid move. I think now it was a stupid move. In a few years, if I were a betting man, I would bet I still think it a stupid move. Boy did they really miss his bat in this lineup, especially when Roman Anthony, one of the aforementioned kids, went down with a month and a half left to play in the season.

Triston Casas fully ruptured his patellar tendon before he even played 30 games when I and many others counted on him to be our slugging first baseman. Instead of 35 home runs, he hit only 3.

Alex Bregman was hurt for about two months and Trevor Story was ice cold for about the same. Campbell was demoted like I said and Roman Anthony’s callup was later than I thought it would be and then he got hurt. Only Duran really performed as I expected.

The team also started the year running and being aggressive on the base paths, but that stopped during the summer. David Hamilton had an awful season and through the middle months was playing in AAA, only returning to the major league club because of the injuries ripping through Boston’s roster.

As for the two rookies I thought would play. most of the year, Campbell’s triple slash fell short of my predictions. Hence his demotion. Roman Anthony’s bat exceeded my expectations, though he was hitting more grounders and ground ball singles than I would have hoped, leading to a lower slugging percentage.

Finally we have the pitching.

Good lord did things go horribly awry.

Houck was bad then blew out his elbow and had Tommy John surgery, meaning he missed 2/3 of this season and will miss all of next year.

I never wanted Buehler, but thought he would be even more serviceable than he turned out to be. He was just awful. Of the expected rotation, that left just three guys: Garrett Crochet, Brayan Bello, and Lucas Giolito.

Crochet was as advertised, a true ace. He led the American League in innings pitched and all of baseball in strikeouts. He dominated. Period. Bello started the year injured as did Giolito, so whislt Bello did eventually hit the innings I expected him to, Giolito did not. Nor did either player reach the strikeout totals I thought they would, though with Bello that may be more due to a change in approach. It seemed as if last year the Sox wanted him to be a strikeout guy. He has shown flashes of it in the past. But this year he really seemed to buy into the ground ball and weak contact guy.

Then in the bullpen and in the ninth inning Aroldis Chapman was just as dominant as Crochet was at the start. Unfortunately, Justin Slaten had some elbow issues with some kind of a bone spur and he missed significant time. Though in his limited innings he looked as dominant as I hoped. Fortunately he just came back in time for the playoffs.

All in all, a good year for the Red Sox.

As for what happens next, well this time I will leave you with my playoff predictions, though I have edited them to actually include the teams that really did make them.

Let’s go Red Sox.

Credit for the pieces is mine.

2025 Red Sox Draft Breakdown

Monday and Tuesday, Major League Baseball conducted its amateur player draft, wherein teams select American university and high school players. They have two weeks to sign them and assign them. (Though many will not actually play this year.)

Two years ago the Red Sox installed Craig Breslow as their new chief baseball organisation. He has cut a number of front office personnel and reorganised the Red Sox front office, leading to a number of departures. Crucially for this context, a number of the scouts who identified key Red Sox players like Roman Anthony were either let go or left. The team then focused on analysts and models.

My questions have thus been focused on how this might change the Red Sox’ approach to the draft. A running joke in Sox circles has been how every year the Red Sox draft a high school shortstop from California. But this year, the Red Sox’ first pick was Kyson Witherspoon, a starting pitcher from Oklahoma.

The graphic above shows how Witherspoon was ranked by the media who covers this niche area of baseball: a consensus top-10 pick. And yet the Sox selected Witherspoon at no. 15 overall. This has been another trend of the Sox over the last several years, where other teams select lower-ranked players and leave higher-ranked players available to the Sox and other mid-round selectors. Similarly, fourth-round pick Anthony Eyanson, ranked roughly 40–65, remained on the board and so the Sox took him at no. 87.

As someone who follows the Sox system, they need quality pitching prospects as they have very few of proven track records in the minors. Witherspoon and Eyanson provide them that, at least the quality, the track records have yet to develop. Marcus Phillips, seemingly, presents more of a lottery ticket. His ranking spread so far, from 13 to 98, it is clear there is no consensus on the type of talent the Sox took in him.

Godbout is a middle-infielder with a good hit tool, but light on the power. Clearly the Sox believe they can work with him to develop the power in the next few years. But all in all, three pitchers in the first four rounds.

Now, the additional context for the non-baseball fans amongst you who are still reading is this. Baseball’s draft does not work in the same way as those of, say the NFL or the NBA. One, the draft is much deeper at 20 rounds. (In my lifetime it used to be as deep as 50.) Two, teams (usually) do not draft for need. I.e., unlike the NFL where a team , say the Patriots, who needs a wide receiver might draft a wide receiver with their first pick, a team like the Red Sox who need, say, a catcher will not draft a catcher. A key reason why, it takes years for an MLB draftee to reach the majors if he does so at all. Whereas an NFL draftee likely plays for the Patriots the following year. In short, there is often a lag between the draft and the debut—unless you are the Los Angeles Angels. Thus you address your current positional needs via free agency or trades, not the draft. (Unless you are the Angels.) For the purposes of the draft, you therefore draft the “best player available” (BPA).

Some systems, however, are just better at doing different things. Some teams do a better job of developing pitchers, others of developing hitters. Some of developing certain traits of pitching or hitting. Some teams are just bad at it overall. The Sox have, of late, been very good at developing position players/hitters. They have been pretty not-so-great at developing pitching. Hence, when Breslow said he could improve their pitching pipeline, the Sox jumped at the chance to hire him. (It also helps everyone else they interviewed said no, and a number of candidates declined to even be interviewed.)

In part, the failure to develop pitching could be a failure to identify the correct player traits or characteristics. It could be the wrong methods and strategies, improper techniques and technologies. But, if we look at the recent history of Red Sox drafts, it could be, in part, also a consistent lack of drafting pitching. After all, the 26-man MLB team roster comprises 14 pitchers and 12 position players. (Technically it is a limit of 14 pitchers, but teams seem to generally max out their pitcher limit.)

You can see in my graphic above, since the late 2000s, the Red Sox, with few exceptions, ever drafted more than 50% pitchers. This period of time coincides with the ascendance of the vaunted Sox position player development factory and the decline of the homegrown starter. (Again, the obligatory reminder correlation is not causation.)

Nevertheless, in the last few years, we have seen the drafting of pitchers spike. In the first two years of the new Breslow regime, pitchers represent more than 70% of the amateur draft. (There is also the international signing period where players from around the world can be signed within limits. This is how the Sox have drafted very talented players like Rafael Devers and Xander Bogaerts. I omitted this talent acquisition channel from the graphics.)

Consequently, when a team states its strategy is to draft the BPA, but over 70% of all players selected are pitchers, I wonder how one defines “best”. Are the Red Sox weighing pitching more heavily than hitting? Is this an attempt to address a long-standing asymmetry in talent? In the models teams like the Red Sox use, are pitchers worth, say, 1.5× more than hitters? I doubt we will ever know the answer, though the team maintains they draft the best player available.

Ultimately, it may matter very little for the Red Sox in the near-term. The sport’s best prospect, Roman Anthony, is just starting to man the outfield for the Sox. A consensus top-10 prospect, Marcelo Mayer, has also just debuted. A top-25 prospect, Kristian Campbell, debuted on Opening Day. Two second-year players round out the outfield in Ceddanne Rafaela and Wilyer Abreu. A rookie catcher is behind the plate. The Sox may not need serious high-end positional player talent in the next 3–5 years. (Though it certainly helps when trying to trade for other pieces.)

But a two-year lull in drafting high-end positional player talent, on top of the previous two years’ first-round draft picks, catcher Kyle Teal and outfielder Braden Montgomery, being traded for ace Garrett Crochet, means the Sox may well have a several-year gap in positional player matriculation to the majors. That might matter.

Baseball, unlike the NFL and the NBA, is a marathon, however. So perhaps this is all a tempest in a teapot. Let us check back in five years’ time and we can see whether this new draft strategy, if it is indeed a strategy, has cost the Red Sox anything.

Credit for the pieces is mine.

A Refreshed Look at My Ethnic Heritage

Late last week I received an update on my ethnic breakdown from My Heritage, a competitor of Ancestry.com and other genealogy/family history/genetic ancestry companies. For many years, the genealogical community had been waiting for this long-promised update. And it has finally arrived.

For my money, My Heritage’s older analysis, v0.95, did not align with my historical record research—something I have done for almost 15 years now. That DNA analysis painted me with an 85% heritage of Irish, Scottish, and Welsh. Because I have spent a decade and a half researching my ancestors, I know all of my second-great-grandparents, 16 total. 85% means 13–14 of them would be Irish, Scottish, or Welsh. However, four of them are Carpatho-Rusyns from present day eastern Slovakia. And nowhere in my research have I found any connection to the Baltic states or Finland.

Compare that to the update.

Here we have a drastically reduced Irish component that, importantly, has been split from Scottish and Welsh, which now exists as its own genetic group. The East European group appears too low, but perhaps My Heritage identified some of my Slavic ancestry as Balkan—there is a sizeable Carpatho-Rusyn community in Vojvodina, an autonomous oblast in Serbia. Maybe Germanic too? That would start to push it near to 20%.

I do have English ancestry—my Angophilia must come from somewhere—though it is relatively small and I can trace it to the Medieval period. That includes more of the Norman elite than the Anglo-Saxon plebs and so seeing Breton register could be indicative of that Norman/Anglo-Saxon population mixture.

But how does My Heritage results compare to those provided by Ancestry and FamilyTreeDNA, two competitors whose services I have also used. And how does it compare to my actual historical document research?

My Heritage’s newest analysis certainly hits a lot better and is nearer to Ancestry, which aligns best with my research. I do have two questions for my second-great-grandparents. One surrounds Nathaniel Miller, one of whose grandparents (Eliza Garrotson) may not be English but rather Dutch from the Dutch colonisation of the Hudson River Valley in New York south of Albany.

The other question revolves around William Doyle. His mother is identified in the records variously as English and Irish. A family story on that side of the family also suggests one ancestor of English descent. And finally, a recently discovered marriage record for his parents details how his mother (Martha Atkins) was baptised and converted to Catholicism as an adult prior to her marriage. Not all Irish are Catholic, but the vast majority are and that would also suggest Martha was not Irish.

Taking those two questions into account, I have a small range of expected values for my English ancestry and a slightly larger one for my Irish and you can see those in the graphic.

When you compare that to the My Heritage results alongside the Ancestry and FamilyTreeDNA results you can see Ancestry aligns best with my research whereas FamilyTreeDNA aligns the least. My Heritage now falls squarely between the two. And so I consider their update a success. I think the company still has some work to do, but progress is progress.

Credit for the pieces is mine.

The Great British Baking

Recently the United Kingdom baked in a significant heatwave. With climate change being a real thing, an extreme heat event in the summer is not terribly surprising. Also not surprisingly, the BBC posted an article about the impact of climate change.

The article itself was not about the heatwave, but rather the increasing rate of sea level rise in response to climate change. But about halfway down the article the author included this graphic.

As graphics go, it is not particularly fancy—a dot plot with ten points labelled. But what this piece does well is using a dot plot instead of the more common bar chart. I most typically see two types of charts when plotting “hottest days” or something similar. The first is usually a simple timeline with a dot or tick indicating when the event occurred. Second, I will sometimes see a bar chart with the hottest days presented all as bars, usually not in the proper time sequence, i.e. clustered bar next to bar next to bar.

My issue with the the latter is always where is the designer placing the bottom of the bar? When we look at the best temperature graphics, we usually refer to box plots wherein the bar is aligned to the day and then top of the bar is the daily high and the bottom of the bar the daily low. It does not make sense to plot temperatures starting at, say 0º.

In this particular case, however, the dates would appear to overlap too closely to allow a proper box plot. Though I suspect—and would be curious to see—if the daily minimum temperatures on each of those ten hottest days have also increased in temperature.

As to the timeline option, this does a better job of showing not just the increasing frequency of the hottest days, but also the rising maximum value. In the early 20th century the hottest day was 36.7ºC, and you can see a definite trend towards the hottest days nearing and finally surpassing 40ºC.

I do wonder if a benchmark line could have been added to the chart, e.g. the summertime average daily high or something similar. Or perhaps a line showing each day’s temperature faintly in the background.

Finally, I want to point out the labelling. Here the designers do a nice job of adding a white stroke or outline to the outside of the text labels. This allows the text to sit atop the y-axis lines and not have the lines interfere with the text’s legibility. That’s always a nice feature to see.

Credit for the piece goes to the BBC graphics department.

Graduate Degrees

Many of us know the debt that comes along with undergraduate degrees. Some of you may still be paying yours down. But what about graduate degrees? A recent article from the Wall Street Journal examined the discrepancies between debt incurred in 2015–16 and the income earned two years later.

The designers used dot plots for their comparisons, which narratively reveal themselves through a scrolling story. The author focuses on the differences between the University of Southern California and California State University, Long Beach. This screenshot captures the differences between the two in both debt and income.

Some simple colour choices guide the reader through the article and their consistent use makes it easy for the reader to visually compare the schools.

From a content standpoint, these two series, income and debt, can be combined to create an income to debt ratio. Simply put, does the degree pay for itself?

What’s really nice from a personal standpoint is that the end of the article features an exploratory tool that allows the user to search the data set for schools of interest. More than just that, they don’t limit that tool to just graduate degrees. You can search for undergraduate degrees.

Below the dot plot you also have a table that provides the exact data points, instead of cluttering up the visual design with that level of information. And when you search for a specific school through the filtering mechanism, you can see that school highlighted in the dot plot and brought to the top of the table.

Fortunately my alma mater is included in the data set.

Unfortunately you can see that the data suggests that graduates with design and applied arts degrees earn less (as a median) than they spend to obtain the degree. That’s not ideal.

Overall this was a really nice, solid piece. And probably speaks to the discussions we need to have more broadly about post-secondary education in the United States. But that’s for another post.

Credit for the piece goes to James Benedict, Andrea Fuller, and Lindsay Huth.

Philadelphia’s Wild Winters

Winter is coming? Winter is here. At least meteorologically speaking, because winter in that definition lasts from December through February. But winters in Philadelphia can be a bit scattershot in terms of their weather. Yesterday the temperature hit 19ºC before a cold front passed through and knocked the overnight low down to 2ºC. A warm autumn or spring day to just above freezing in the span of a few hours.

But when we look more broadly, we can see that winters range just that much as well. And look the Philadelphia Inquirer did. Their article this morning looked at historical temperatures and snowfall and whilst I won’t share all the graphics, it used a number of dot plots to highlight the temperature ranges both in winter and yearly.

The screenshot above focuses attention on the range in January and July and you can see how the range between the minimum and maximum is greater in the winter than in the summer. Philadelphia may have days with summer temperatures in the winter, but we don’t have winter temperatures in summer. And I say that’s unfair. But c’est la vie.

Design wise there are a couple of things going on here that we should mention. The most obvious is the blue background. I don’t love it. Presently the blue dots that represent colder temperatures begin to recede into and blend into the background, especially around that 50ºF mark. If the background were white or even a light grey, we would be able to clearly see the full range of the temperatures without the optical illusion of a separation that occurs in those January temperature observations.

Less visible here is the snowfall. If you look just above the red dots representing the range of July temperatures, you can see a little white dot near the top of the screenshot. The article has a snowfall effect with little white dots “falling” down the page. I understand how the snowfall fits with the story about winter in Philadelphia. Whilst the snowfall is light enough to not be too distracting, I personally feel it’s a bit too cute for a piece that is data-driven.

The snowfall is also an odd choice because, as the article points out, Philadelphia winters do feature snowfall, but that on days when precipitation falls, snow accounts for less than 1/3 of those days with rain and wintry mixes accounting for the vast majority.

Overall, I really like the piece as it dives into the meteorological data and tries to accurately paint a portrait of winters in Philadelphia.

And of course the article points out that the trend is pointing to even warmer winters due to climate change.

Credit for the piece goes to Aseem Shukla and Sam Morris.

Those Are Some Heavy Balls

Unfortunately, I don’t subscribe to Business Insider, but I saw this graphic on the Twitter and felt the need to share it. Primarily because baseball will almost certainly stop at midnight when the owners of the teams will impose a lockout (as opposed to players going on strike). And with that baseball will be on hold until the two parties resolve their current labour issues.

And at present that seems like it could take quite some time.

So on the eve of the lockout Bradford William Davis tweeted a link to an article he wrote, alas no subscription as aforementioned, but he did share one of the graphics therein.

We have a basic dot plot charting the weight of the centre of baseballs, sorted by the month of game from which they were pulled.

The designer made a few interesting choices here. First, typographically, we have a few decisions around the type. I would have loved to have seen a bit of editing or design to eliminate the widow at the end of the graphic’s subtitle, that bit that just says “(blue)”. Do the descriptors in parentheses even need to be there when the designer included a legend immediately below? I find that one word incredibly distracting.

On the other hand, the designer chose to use a thin white outline around the text on the plot. Normally I’d really like this choice, because it can reduce some of the issues around legibility when lines intersect text, especially when they are the same colour. Here, however, the backgrounds are not white. I would have tried, for the top, using that light blue instead of white as the stroke for the outside of the letters. And on the bottom I would have tried the light pink. That would probably achieve the presumed desired effect of reducing the visual interference unintentionally created by the white. I also would have moved the top label up so it didn’t sit overlay the top dot.

As far as the dot plot itself goes, that works fine. I wonder if some transparency in the dots would have emphasised how many dots sit atop each other. Or maybe they could have clustered, but when overlapping moved horizontally off the vertical axis.

Overall this was a really nice graphic with which to end this half of the baseball off season. Hopefully the lockout doesn’t last too long.

Credit for the piece goes to Taylor Tyson.

Low Expectations

Today the 2021 Major League Baseball season begins its playoffs. Tomorrow we get the Los Angeles Dodgers and the St. Louis Cardinals. Why the Dodgers, the team with the second-best record in all of baseball, need to play a one-game play-in is dumb, but a subject for perhaps another post. Tonight, however, is the American League (AL) Wildcard game and it features one of the best rivalries in baseball if not American sports: the Boston Red Sox vs. the New York Yankees.

Full disclosure, as many of you know, I’m a Sox fan and consider the Yankees the Evil Empire. But at the beginning of the year, the consensus around the sport was that the Yankees would win first place in their division and be followed by the Tampa Bay Rays or the Toronto Blue Jays. The Red Sox would place fourth and the lowly Baltimore Orioles fifth. The Red Sox, as the consensus went, were, after gutting their team of top-flight talent and a no-good, rotten, despicable 2020 showing, nowhere near ready to reach the playoffs. The Yankees were an unstoppable offensive juggernaut.

When the 2021 season ended Sunday night, as the dust around home plate settled, the Rays dominated the AL East to take first. But it was the Red Sox that finished second and the Yankees who took third. Whilst the two teams had the same record, in head-t0-head match-ups the Red Sox won more games than the Yankees, 10–9. Not bad for a team that everyone thought couldn’t make the playoffs and would be in fourth place.

That got me thinking though, how wrong were our expectations? After doing some Googling to find individual reports and finding a Red Sox twitter account (@RedSoxStats) that captured as many preseason forecasts as he could, I was ready to make a chart. The caveat here is that we don’t have data for all beat writers, who cover the Red Sox exclusively or almost exclusively on a daily basis, or even national media writers, who cover the Red Sox along with the rest of the sport and its teams. For example, ESPN polled 37 of its writers, but all we know is that 0 of 37 expected the Red Sox to make the playoffs. I don’t have a single estimate for the number of wins, which obviously determines who gets into said playoffs, for those 37 forecasts. Others, like CBS Sports, broke down each of their five writers’ rankings for the division and all five had the Red Sox finishing fourth. But again, we don’t have numbers of wins. So in a sense, if we could get numbers from back in the winter and early spring, this chart would look even crazier with the Red Sox being even more outperform-ier than they do here.

We should also remember that during September, in the lead-up to the playoffs, the Red Sox were struggling with a Covid-19 outbreak that put nearly half their starting roster on the Injured List (IL). The Sox had the backups to the backups starting alongside the backups, some of whom then also went on the IL with Covid-19 leading to signings of players who, despite being integral to the September success, are not eligible to play in the playoffs due to when they signed. José Iglesias brought some 2013 magic to be sure. Earlier in the year, MLB would postpone games when significant numbers of players were unavailable, but the Red Sox, for whatever reason, had to play every game. And there were instances where players started the game, but in the middle of the game their tests came back positive and they had to be removed from the field in the middle of the game.

I’m not certain where I stand on how much managers influence the win-loss record in baseball. But if the Sox manager, Alex Cora, doesn’t at least get some nods for being manager of the year, I’ll be truly shocked.

The Red Sox are not a great team. This is not the 2018 behemoth, but rather an early rebuild for a hopefully competitive team in 2023. Their defence is not great. They lack depth in the rotation and the bullpen. I, for one, never doubted their offence—2020 surely had to have been a pandemic fluke. But I had serious questions about their starting rotation. Ultimately the rotation proved itself to be…adequate. And while they played through Covid-19 and kept their heads above water in September, the last few weeks were, at times, hard to watch. The Yankees swept them at Fenway, site of tonight’s game, just last weekend. Of late, the Yankees have been the better team. And all year long, the Red Sox played less competitively than I’d like against the other teams that made the playoffs.

I don’t expect them to win let alone make the World Series, but nobody expected them to be here anyway. Maybe they still have a few more surprises in them. After all, anything can happen in October baseball.

Credit for the piece is mine.

Updated DNA Ethnicity Estimates

Earlier this year I posted a short piece that compared my DNA ethnicity estimates provided by a few different companies to each other. Ethnicity estimates are great cocktail party conversations, but not terribly useful to people doing serious genealogy research. They are highly dependent upon the available data from reference populations.

To put it another way, if nobody in a certain ethnic group has tested with a company, there’s no real way for that company to place your results within that group. In the United States, Native Americans are known for their reluctance to participate and, last I heard, they are under-represented in ethnicity estimates. Fortunately for me, Western European population groups are fairly well tested.

But these reference populations are constantly being updated and new analysis being performed to try and sort people into ever more distinct genetic communities. (Although generally speaking the utility of these tests only goes back a handful of generations.)

Last night, when working on a different post, I received an email saying Ancestry.com had updated their analysis of my DNA. So naturally I wanted to compare this most recent update to last September’s.

Sometimes when you look at data and create data visualisation pieces, the story is that there is very little change. And that’s my story. The actual number for my Irish estimate remained the same: 63%. I saw a slight change to my Scottish and Slavic numbers, but nothing drastic. My trace results changed, switching from 2% from the Balkans to 2% from Sweden and Denmark. But you need to take trace results with a pretty big grain of salt, unless they are of a different continent. Broadly speaking, we can be fairly certain about results at a continental level, but differences between, say, French and Germans are much harder to distinguish.

The Scottish part still fascinates me, because as far back as I’ve gone, I have not found an identifiable Scottish ancestor. A great-great-grandfather lived for several years in Edinburgh, but he was the son of two Ireland-born Irish parents. I also know that this Scottish part of me must come from my paternal lines as my mother has almost no Scottish DNA and she would need to have some if I were to have had inherited it from her.

Now for about half of my paternal Irish ancestors, I know at least the counties from which they came. My initial thought, and still best guess, is that the Scottish is actually Scotch–Irish from what is today Northern Ireland. But I am unaware of any ancestor, except perhaps one, who came from or has origins in Northern Ireland.

The other thing that fascinated me is that despite the additional data and analysis the ranges, or degree of uncertainty in another way of looking at it, increased in most of the ethnicities. You can see the light purple rectangles are actually almost all larger this year compared to last. I can only wonder if this time next year I’ll see any narrowing of those ranges.

Credit for the piece is mine.

The Pandemic of the Unvaccinated

Get your shots.

It’s pretty much that simple. But for just under half the country, it’s not getting through. So I went looking for some data on the breakdown of Covid-19 cases by vaccinated and unvaccinated people.

I found an analysis by the Kaiser Family Foundation (KFF), a non-profit that focuses on health and healthcare issues. They collected the data made available by 24 states—not all states provide a breakdown of breakthrough cases—and what we see across the country is pretty clear. If you want more details on their methodology, I highly recommend you check out their analysis.

In all but Arizona and Alaska, vaccinated people account for less than 4% of Covid-19 cases. In most of these states, it’s less than 2%. For the states that we regularly cover here—Pennsylvania, New Jersey, Delaware, Virginia, and Illinois—we have New Jersey, Delaware, and Virginia represented in the data set.

Delaware leads the three with vaccinated people accounting for just 1% of Covid-19 cases. Virginia is 0.7% and New Jersey is just 0.2%. In other words, in New Jersey almost nobody vaccinated is catching Covid-19 over the observation period.

And when we look at the vaccinated population, we can see what breakthrough events—cases, hospitalisations, and deaths—they are experiencing.

In almost all states, less than 0.5% of vaccinated people are getting Covid-19. Only in Arkansas do we see a number greater than that: 0.54%. In no state do we have more than 0.6% of vaccinated people requiring hospitalisation. And with that number so low, it won’t surprise you that in no state do we have more than 0.01% of vaccinated people dying.

In other words, the rapidly climbing numbers of new cases and slowly rising deaths that we looked at yesterday, that’s almost all in people who haven’t yet gotten vaccinated.

Get your shots.

Credit for the piece is mine.